In this paper, we present an efficient approach for unsupervised segmentation of natural and textural images based on the extraction of image features and a fast active contour segmentation model. We address the probl...In this paper, we present an efficient approach for unsupervised segmentation of natural and textural images based on the extraction of image features and a fast active contour segmentation model. We address the problem of textures where neither the gray-level information nor the boundary information is adequate for object extraction. This is often the case of natural images composed of both homogeneous and textured regions. Because these images cannot be in general directly processed by the gray-level information, we propose a new texture descriptor which intrinsically defines the geometry of textures using semi-local image information and tools from differential geometry. Then, we use the popular Kullback-Leibler distance to design an active contour model which distinguishes the background and textures of interest. The existence of a minimizing solution to the proposed segmentation model is proven. Finally, a texture segmentation algorithm based on the Split-Bregrnan method is introduced to extract meaningful objects in a fast way. Promising synthetic and real-world results for gray-scale and color images are presented.展开更多
The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder （KL-SAE） is...The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder （KL-SAE） is proposed in this paper, which can be applied to battle damage assessment （BDA）. This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University ofCalifomia-Irvine （UCI） data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.展开更多
The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally diffi...The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally difficult to handle the above problems simultaneously,such as the Track-Oriented marginal Multi-Bernoulli/Poisson(TOMB/P)and Measurement-Oriented marginal Multi-Bernoulli/Poisson(MOMB/P)filters.Based on the Arithmetic Average(AA)fusion rule,this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli(PMB)filter,which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with coalescence.In order to fuse the different PMB distributions,the Bernoulli components in different Multi-Bernoulli(MB)distributions are associated with each other by Kullback-Leibler Divergence(KLD)minimization.Moreover,an adaptive AA fusion rule is designed on the basis of the exponential fusion weights,which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in MTT.Finally,by comparing with the TOMB/P and MOMB/P filters,the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.展开更多
This paper attempts to present an interactive color natural images segmentation method. This method extracts the feature of images by using the nonlinear compact structure tensor （NCST） and then uses GrabCut method ...This paper attempts to present an interactive color natural images segmentation method. This method extracts the feature of images by using the nonlinear compact structure tensor （NCST） and then uses GrabCut method to obtain the segmentation. This method not only realizes the non-parametric fusion of texture information and color information, but also improves the efficiency of the calculation. Then, the improved GrabCut algorithm is used to evaluate the foreground target segmentation. In order to calculate the simplicity and efficiency, this paper also extends the Gaussian mixture model （GMM） constructed base on the GrabCut to the tensor space, and uses the Kullback-Leibler （KL） divergence instead of the usual Riemannian geometry. Lastly, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. After conducting a large number of experiments on synthetic texture images and natural images, the results demonstrate that this method has a more accurate segmentation effect.展开更多
For the target threat evaluation of warships formation air defense, the sample data are frequently insufficient and even incomplete. The existing evaluation methods rely too much on expertise and are difficult to carr...For the target threat evaluation of warships formation air defense, the sample data are frequently insufficient and even incomplete. The existing evaluation methods rely too much on expertise and are difficult to carry out for the dynamic evaluation on time series. In order to solve these problems, a threat evaluation method based on the AR(p)(auto regressive(AR))-dynamic improved technique for order preference by similarity to ideal solution(DITOPSIS) method is proposed. The AR(p) model is adopted to predict the missing data on the time series. Then, the entropy weight method is applied to solve each index weight at the objective point. Kullback-Leibler divergence(KLD) is used to improve the traditional TOPSIS, and to carry out the target threat evaluation. The Poisson distribution is used to assign the weight value.Simulation results show that the improved AR(p)-DITOPSIS threat evaluation method can synthetically take into account the target threat degree in time series and is more suitable for the threat evaluation under the condition of missing the target data than the traditional TOPSIS method.展开更多
A Kullback-Leibler（KL）distance based algorithm is presented to find the matches between concepts from different ontologies. First, each concept is represented as a specific probability distribution which is estimate...A Kullback-Leibler（KL）distance based algorithm is presented to find the matches between concepts from different ontologies. First, each concept is represented as a specific probability distribution which is estimated from its own instances. Then, the similarity of two concepts from different ontologies is measured by the KL distance between the corresponding distributions. Finally, the concept-mapping relationship between different ontologies is obtained. Compared with other traditional instance-based algorithms, the computing complexity of the proposed algorithm is largely reduced. Moreover, because it proposes different estimation and smoothing methods of the concept distribution for different data types, it is suitable for various concepts mapping with different data types. The experimental results on real-world ontology mapping illustrate the effectiveness of the proposed algorithm.展开更多
In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of ...In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.展开更多
To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles (HRRPs), in this paper, the average range profile and the variance range profile are extracted together...To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles (HRRPs), in this paper, the average range profile and the variance range profile are extracted together as the feature vectors for both training data and test data representa-tion. And a decision rule is established for Automatic Target Recognition (ATR) based on the mini-mum Kullback-Leibler Distance (KLD) criterion. The recognition performance of the proposed method is comparable with that of Adaptive Gaussian Classifier (AGC) with multiple test HRRPs, but the proposed method is much more computational efficient. Experimental results based on the measured data show that the minimum KLD classifier is effective.展开更多
Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper co...Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial （Li et al., 2015）. First, identical distribution （ID） is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent met- rics including the （symmetrical） Kullback-Leibler divergence, Kolmogorov-Smimov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive under- standing of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations展开更多
Mobile Ad-hoc Networks(MANET)usage across the globe is increas-ing by the day.Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes...Mobile Ad-hoc Networks(MANET)usage across the globe is increas-ing by the day.Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes.The trust values are estimated based on the reputation values of each node in the network by using different mechanisms.However,these mechanisms have various challenging issues which degrade the network performance.Hence,a novel Quality of Service(QoS)Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work.Initially,the QoS-based trust estimation is proposed by using a Fuzzy logic scheme.The trust value of each node is estimated by using each node’s reputation values which are deter-mined based on the fuzzy membership function values and utilizing QoS para-meters such as residual energy,bandwidth,node mobility,and reliability.This mechanism prevents only the black hole attack in the network during transmis-sion.But,the gray hole attacks are not identified which in turn increases the pack-et drop rate significantly.Hence,the gray hole attack is also detected based on the Kullback-Leibler(KL)divergence method used for estimating the statistical mea-sures.Additional QoS metrics are considered to prevent the gray hole attack,such as packet loss,packet delivery ratio,and delay for each node.Thus,the proposed mechanism prevents both black hole and gray hole attacks simultaneously.Final-ly,the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.展开更多
We explored the possibilities of whole-genome duplication （WGD） in prokaryotic species, where we performed statistical analyses of the configurations of the central angles between homologous tandem repeats （TRs） o...We explored the possibilities of whole-genome duplication （WGD） in prokaryotic species, where we performed statistical analyses of the configurations of the central angles between homologous tandem repeats （TRs） on the circular chromosomes. At first, we detected TRs on their chromosomes and identified equivalent tandem repeat pairs （ETRPs）; here, an ETRP is defined as a pair of tandem repeats sequentially similar to each other. Then we carried out statistical analyses of the central angle distributions of the detected ETRPs on each circular chromosome by way of comparisons between the detected distributions and those generated by null models. In the analyses, we estimated a P value by a simulation using the Kullback-Leibler divergence as a distance measure between two distributions. As a result, the central angle distributions for 8 out of the 203 prokaryotic species showed statistically significant deviations （P〈0.05）. In particular, we found out the characteristic feature of one round of WGD in Photorhabdus luminescens genome and that of two rounds of WGD in Escherichia coli K12.展开更多
Accurate reconstruction from a reduced data set is highly essential for computed tomography in fast and/or low dose imaging applications. Conventional total variation（TV）-based algorithms apply the L1 norm-based pen...Accurate reconstruction from a reduced data set is highly essential for computed tomography in fast and/or low dose imaging applications. Conventional total variation（TV）-based algorithms apply the L1 norm-based penalties, which are not as efficient as Lp（0〈p〈1） quasi-norm-based penalties. TV with a p-th power-based norm can serve as a feasible alternative of the conventional TV, which is referred to as total p-variation（TpV）. This paper proposes a TpV-based reconstruction model and develops an efficient algorithm. The total p-variation and Kullback-Leibler（KL） data divergence, which has better noise suppression capability compared with the often-used quadratic term, are combined to build the reconstruction model. The proposed algorithm is derived by the alternating direction method（ADM） which offers a stable, efficient, and easily coded implementation. We apply the proposed method in the reconstructions from very few views of projections（7 views evenly acquired within 180°）. The images reconstructed by the new method show clearer edges and higher numerical accuracy than the conventional TV method. Both the simulations and real CT data experiments indicate that the proposed method may be promising for practical applications.展开更多
This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span>&...This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span><span "=""><span>we seek a </span><span>near perfect</span><span> translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of </span><span>stationary</span><span> model is too restrictive especially for long time series. The estimator is shown to be consistent through asymptotic theory and finally proven through simulations. The estimator is applied to the generalized Pareto distribution to estimate changes in the scale and shape parameters.</span></span>展开更多
The objective of this paper is to present a Bayesian approach based on Kullback- Leibler divergence for assessing local influence in a growth curve model with general co- variance structure. Under certain prior distri...The objective of this paper is to present a Bayesian approach based on Kullback- Leibler divergence for assessing local influence in a growth curve model with general co- variance structure. Under certain prior distribution assumption, the Kullback-Leibler di- vergence is used to measure the influence of some minor perturbation on the posterior distribution of unknown parameter. This leads to the diagnostic statistic for detecting which response is locally influential. As an application, the common covariance-weighted perturbation scheme is thoroughly considered.展开更多
This study propose a new robust method to rank the performances of multi-assets （portfolios）, based purely on their return time series. This method makes no assumption on the distributions. Topsoe distance is symmet...This study propose a new robust method to rank the performances of multi-assets （portfolios）, based purely on their return time series. This method makes no assumption on the distributions. Topsoe distance is symmetrized Kullback-Leibler divergence by average of the probabilities. The square root of Topsoe distance is a metric. We extend this metric from probability density functions to real number series on （0, 1 ]. We call it ST-metric. We show the consistency of ST-metric with mean-variance theory and stochastic dominance method of order one and two. We demonstrate the advantages of ST-metric over mean-variance rule and stochastic dominance method of order one and two.展开更多
We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian Kalman filter.The natural gradient approach is applied to the Kullback-Leibler divergence...We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian Kalman filter.The natural gradient approach is applied to the Kullback-Leibler divergence between the parameterized variational distribution and the posterior density of interest.Using a Gaussian assumption for the parametrized variational distribution,we obtain a closed-form iterative procedure for the Kullback-Leibler divergence minimization,producing estimates of the variational hyper-parameters of state estimation and the associated error covariance.Simulation results in both a Doppler radar tracking scenario and a bearing-only tracking scenario are presented,showing that the proposed natural gradient method outperforms existing methods which are based on other linearization techniques in terms of tracking accuracy.展开更多
We propose to approximate the conditional density function of a random variable Y given a dependent random d-vector X by that of Y given θ^τX, where the unit vector θ is selected such that the average Kullback-Leib...We propose to approximate the conditional density function of a random variable Y given a dependent random d-vector X by that of Y given θ^τX, where the unit vector θ is selected such that the average Kullback-Leibler discrepancy distance between the two conditional density functions obtains the minimum. Our approach is nonparametric as far as the estimation of the conditional density functions is concerned. We have shown that this nonparametric estimator is asymptotically adaptive to the unknown index θ in the sense that the first order asymptotic mean squared error of the estimator is the same as that when θ was known. The proposed method is illustrated using both simulated and real-data examples.展开更多
In this paper, we study the diagnostic problems of parametric empirical Bayes models systematically. Cook’s distance and Kullback-Leibler divergence are used to measure the effect of each individual observation, and ...In this paper, we study the diagnostic problems of parametric empirical Bayes models systematically. Cook’s distance and Kullback-Leibler divergence are used to measure the effect of each individual observation, and the local influence diagnostic is used to assess the influence of minor perturbations on empirical Bayes estimates as well. Besides the deletion approach, the idea of local influence is applied to examine the impact of the regression variable. Lastly, some numerical examples are presented to illustrate our approach.展开更多
基金supported by Swiss National Science Foundation Grant #205320-101621supported by ONR N00014-03-1-0071
文摘In this paper, we present an efficient approach for unsupervised segmentation of natural and textural images based on the extraction of image features and a fast active contour segmentation model. We address the problem of textures where neither the gray-level information nor the boundary information is adequate for object extraction. This is often the case of natural images composed of both homogeneous and textured regions. Because these images cannot be in general directly processed by the gray-level information, we propose a new texture descriptor which intrinsically defines the geometry of textures using semi-local image information and tools from differential geometry. Then, we use the popular Kullback-Leibler distance to design an active contour model which distinguishes the background and textures of interest. The existence of a minimizing solution to the proposed segmentation model is proven. Finally, a texture segmentation algorithm based on the Split-Bregrnan method is introduced to extract meaningful objects in a fast way. Promising synthetic and real-world results for gray-scale and color images are presented.
基金Project supported by the National Basic Research Program （973） of China （No. 61331903） and the National Natural Science Foundation of China （Nos. 61175008 and 61673265）
文摘The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder （KL-SAE） is proposed in this paper, which can be applied to battle damage assessment （BDA）. This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University ofCalifomia-Irvine （UCI） data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.
基金supported by the National Natural Science Foundation of China(No.61871301)。
文摘The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally difficult to handle the above problems simultaneously,such as the Track-Oriented marginal Multi-Bernoulli/Poisson(TOMB/P)and Measurement-Oriented marginal Multi-Bernoulli/Poisson(MOMB/P)filters.Based on the Arithmetic Average(AA)fusion rule,this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli(PMB)filter,which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with coalescence.In order to fuse the different PMB distributions,the Bernoulli components in different Multi-Bernoulli(MB)distributions are associated with each other by Kullback-Leibler Divergence(KLD)minimization.Moreover,an adaptive AA fusion rule is designed on the basis of the exponential fusion weights,which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in MTT.Finally,by comparing with the TOMB/P and MOMB/P filters,the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.
基金supported by the National Natural Science Foundation of China (61372148,61502036,61571045,71373023)the Beijing Advanced Innovation Center for Imaging Technology (BAICIT-2016002)the National Science and Technology Support Program (2014BAK08B02,2015BAH55F03)
文摘This paper attempts to present an interactive color natural images segmentation method. This method extracts the feature of images by using the nonlinear compact structure tensor （NCST） and then uses GrabCut method to obtain the segmentation. This method not only realizes the non-parametric fusion of texture information and color information, but also improves the efficiency of the calculation. Then, the improved GrabCut algorithm is used to evaluate the foreground target segmentation. In order to calculate the simplicity and efficiency, this paper also extends the Gaussian mixture model （GMM） constructed base on the GrabCut to the tensor space, and uses the Kullback-Leibler （KL） divergence instead of the usual Riemannian geometry. Lastly, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. After conducting a large number of experiments on synthetic texture images and natural images, the results demonstrate that this method has a more accurate segmentation effect.
基金supported by the Postdoctoral Science Foundation of China(2013T60923)
文摘For the target threat evaluation of warships formation air defense, the sample data are frequently insufficient and even incomplete. The existing evaluation methods rely too much on expertise and are difficult to carry out for the dynamic evaluation on time series. In order to solve these problems, a threat evaluation method based on the AR(p)(auto regressive(AR))-dynamic improved technique for order preference by similarity to ideal solution(DITOPSIS) method is proposed. The AR(p) model is adopted to predict the missing data on the time series. Then, the entropy weight method is applied to solve each index weight at the objective point. Kullback-Leibler divergence(KLD) is used to improve the traditional TOPSIS, and to carry out the target threat evaluation. The Poisson distribution is used to assign the weight value.Simulation results show that the improved AR(p)-DITOPSIS threat evaluation method can synthetically take into account the target threat degree in time series and is more suitable for the threat evaluation under the condition of missing the target data than the traditional TOPSIS method.
文摘A Kullback-Leibler（KL）distance based algorithm is presented to find the matches between concepts from different ontologies. First, each concept is represented as a specific probability distribution which is estimated from its own instances. Then, the similarity of two concepts from different ontologies is measured by the KL distance between the corresponding distributions. Finally, the concept-mapping relationship between different ontologies is obtained. Compared with other traditional instance-based algorithms, the computing complexity of the proposed algorithm is largely reduced. Moreover, because it proposes different estimation and smoothing methods of the concept distribution for different data types, it is suitable for various concepts mapping with different data types. The experimental results on real-world ontology mapping illustrate the effectiveness of the proposed algorithm.
基金This work was supported in part by the National Natural Science Foundation of China （Grants Nos. 61170122, 61272210）, by Japan Society for the Promotion of Sciences （JSPS）, by the Natural Science Foundation of Jiangsu Province （BK2011417, BK2011003）, by Jiangsu 333 Expert Engineering Grant （BRA201114-2）, and by 2011 and 2012 Postgraduate Student＇s Creative Research Funds of Jiangsu Province （CXZZ11-0483, CXZZ12-0759）.
文摘In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.
基金Partially supported by the National Natural Science Foundation of China (No.60302009).
文摘To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles (HRRPs), in this paper, the average range profile and the variance range profile are extracted together as the feature vectors for both training data and test data representa-tion. And a decision rule is established for Automatic Target Recognition (ATR) based on the mini-mum Kullback-Leibler Distance (KLD) criterion. The recognition performance of the proposed method is comparable with that of Adaptive Gaussian Classifier (AGC) with multiple test HRRPs, but the proposed method is much more computational efficient. Experimental results based on the measured data show that the minimum KLD classifier is effective.
基金Project supported by the National Natural Science Foundation of China(No.51475383)European Commission MSCA-RISE-2014(No.641794)+1 种基金the Excellent Doctorate Foundation of Northwestern Polytechnical Universitythe Postdoctoral Fellowship of the University of Salamanca
文摘Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial （Li et al., 2015）. First, identical distribution （ID） is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent met- rics including the （symmetrical） Kullback-Leibler divergence, Kolmogorov-Smimov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive under- standing of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations
文摘Mobile Ad-hoc Networks(MANET)usage across the globe is increas-ing by the day.Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes.The trust values are estimated based on the reputation values of each node in the network by using different mechanisms.However,these mechanisms have various challenging issues which degrade the network performance.Hence,a novel Quality of Service(QoS)Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work.Initially,the QoS-based trust estimation is proposed by using a Fuzzy logic scheme.The trust value of each node is estimated by using each node’s reputation values which are deter-mined based on the fuzzy membership function values and utilizing QoS para-meters such as residual energy,bandwidth,node mobility,and reliability.This mechanism prevents only the black hole attack in the network during transmis-sion.But,the gray hole attacks are not identified which in turn increases the pack-et drop rate significantly.Hence,the gray hole attack is also detected based on the Kullback-Leibler(KL)divergence method used for estimating the statistical mea-sures.Additional QoS metrics are considered to prevent the gray hole attack,such as packet loss,packet delivery ratio,and delay for each node.Thus,the proposed mechanism prevents both black hole and gray hole attacks simultaneously.Final-ly,the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.
文摘We explored the possibilities of whole-genome duplication （WGD） in prokaryotic species, where we performed statistical analyses of the configurations of the central angles between homologous tandem repeats （TRs） on the circular chromosomes. At first, we detected TRs on their chromosomes and identified equivalent tandem repeat pairs （ETRPs）; here, an ETRP is defined as a pair of tandem repeats sequentially similar to each other. Then we carried out statistical analyses of the central angle distributions of the detected ETRPs on each circular chromosome by way of comparisons between the detected distributions and those generated by null models. In the analyses, we estimated a P value by a simulation using the Kullback-Leibler divergence as a distance measure between two distributions. As a result, the central angle distributions for 8 out of the 203 prokaryotic species showed statistically significant deviations （P〈0.05）. In particular, we found out the characteristic feature of one round of WGD in Photorhabdus luminescens genome and that of two rounds of WGD in Escherichia coli K12.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61372172 and 61601518)
文摘Accurate reconstruction from a reduced data set is highly essential for computed tomography in fast and/or low dose imaging applications. Conventional total variation（TV）-based algorithms apply the L1 norm-based penalties, which are not as efficient as Lp（0〈p〈1） quasi-norm-based penalties. TV with a p-th power-based norm can serve as a feasible alternative of the conventional TV, which is referred to as total p-variation（TpV）. This paper proposes a TpV-based reconstruction model and develops an efficient algorithm. The total p-variation and Kullback-Leibler（KL） data divergence, which has better noise suppression capability compared with the often-used quadratic term, are combined to build the reconstruction model. The proposed algorithm is derived by the alternating direction method（ADM） which offers a stable, efficient, and easily coded implementation. We apply the proposed method in the reconstructions from very few views of projections（7 views evenly acquired within 180°）. The images reconstructed by the new method show clearer edges and higher numerical accuracy than the conventional TV method. Both the simulations and real CT data experiments indicate that the proposed method may be promising for practical applications.
文摘This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span><span "=""><span>we seek a </span><span>near perfect</span><span> translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of </span><span>stationary</span><span> model is too restrictive especially for long time series. The estimator is shown to be consistent through asymptotic theory and finally proven through simulations. The estimator is applied to the generalized Pareto distribution to estimate changes in the scale and shape parameters.</span></span>
基金Supported by the fund of the Yunnan Education Committee!(NO.9941072)
文摘The objective of this paper is to present a Bayesian approach based on Kullback- Leibler divergence for assessing local influence in a growth curve model with general co- variance structure. Under certain prior distribution assumption, the Kullback-Leibler di- vergence is used to measure the influence of some minor perturbation on the posterior distribution of unknown parameter. This leads to the diagnostic statistic for detecting which response is locally influential. As an application, the common covariance-weighted perturbation scheme is thoroughly considered.
文摘This study propose a new robust method to rank the performances of multi-assets （portfolios）, based purely on their return time series. This method makes no assumption on the distributions. Topsoe distance is symmetrized Kullback-Leibler divergence by average of the probabilities. The square root of Topsoe distance is a metric. We extend this metric from probability density functions to real number series on （0, 1 ]. We call it ST-metric. We show the consistency of ST-metric with mean-variance theory and stochastic dominance method of order one and two. We demonstrate the advantages of ST-metric over mean-variance rule and stochastic dominance method of order one and two.
基金co-supported by the National Natural Science Foundation of China(Nos.61790552 and 61976080)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX201915)。
文摘We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian Kalman filter.The natural gradient approach is applied to the Kullback-Leibler divergence between the parameterized variational distribution and the posterior density of interest.Using a Gaussian assumption for the parametrized variational distribution,we obtain a closed-form iterative procedure for the Kullback-Leibler divergence minimization,producing estimates of the variational hyper-parameters of state estimation and the associated error covariance.Simulation results in both a Doppler radar tracking scenario and a bearing-only tracking scenario are presented,showing that the proposed natural gradient method outperforms existing methods which are based on other linearization techniques in terms of tracking accuracy.
基金supported by US National Science Foundation grant DMS-0704337 National Natural Science Foundation of China(No.10628104)supported by an EPSRC research grant EP/C549058/1
文摘We propose to approximate the conditional density function of a random variable Y given a dependent random d-vector X by that of Y given θ^τX, where the unit vector θ is selected such that the average Kullback-Leibler discrepancy distance between the two conditional density functions obtains the minimum. Our approach is nonparametric as far as the estimation of the conditional density functions is concerned. We have shown that this nonparametric estimator is asymptotically adaptive to the unknown index θ in the sense that the first order asymptotic mean squared error of the estimator is the same as that when θ was known. The proposed method is illustrated using both simulated and real-data examples.
文摘In this paper, we study the diagnostic problems of parametric empirical Bayes models systematically. Cook’s distance and Kullback-Leibler divergence are used to measure the effect of each individual observation, and the local influence diagnostic is used to assess the influence of minor perturbations on empirical Bayes estimates as well. Besides the deletion approach, the idea of local influence is applied to examine the impact of the regression variable. Lastly, some numerical examples are presented to illustrate our approach.
