密度峰值聚类(density peaks clustering,DPC)是一种基于密度的聚类算法,该算法可以直观地确定类簇数量,识别任意形状的类簇,并且自动检测、排除异常点.然而,DPC仍存在些许不足:一方面,DPC算法仅考虑全局分布,在类簇密度差距较大的数据...密度峰值聚类(density peaks clustering,DPC)是一种基于密度的聚类算法,该算法可以直观地确定类簇数量,识别任意形状的类簇,并且自动检测、排除异常点.然而,DPC仍存在些许不足:一方面,DPC算法仅考虑全局分布,在类簇密度差距较大的数据集聚类效果较差;另一方面,DPC中点的分配策略容易导致“多米诺效应”.为此,基于代表点(representative points)与K近邻(K-nearest neighbors,KNN)提出了RKNN-DPC算法.首先,构造了K近邻密度,再引入代表点刻画样本的全局分布,提出了新的局部密度;然后,利用样本的K近邻信息,提出一种加权的K近邻分配策略以缓解“多米诺效应”;最后,在人工数据集和真实数据集上与5种聚类算法进行了对比实验,实验结果表明,所提出的RKNN-DPC可以更准确地识别类簇中心并且获得更好的聚类结果.展开更多
Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a...Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.展开更多
针对传统K近邻分类器在大规模数据集中存在时间和空间复杂度过高的问题,可采取原型选择的方法进行处理,即从原始数据集中挑选出代表原型(样例)进行K近邻分类而不降低其分类准确率.本文在CURE聚类算法的基础上,针对CURE的噪声点不易确定...针对传统K近邻分类器在大规模数据集中存在时间和空间复杂度过高的问题,可采取原型选择的方法进行处理,即从原始数据集中挑选出代表原型(样例)进行K近邻分类而不降低其分类准确率.本文在CURE聚类算法的基础上,针对CURE的噪声点不易确定及代表点分散性差的特点,利用共享邻居密度度量给出了一种去噪方法和使用最大最小距离选取代表点进行改进,从而提出了一种新的原型选择算法PSCURE (improved prototype selection algorithm based on CURE algorithm).基于UCI数据集进行实验,结果表明:提出的PSCURE原型选择算法与相关原型算法相比,不仅能筛选出较少的原型,而且可获得较高的分类准确率.展开更多
Competition mechanism in multiple four-wave mixing （MFWM） processes is demonstrated theoretically. Provided considering only two waves injected into a highly nonlinear fiber （HNLF）, there are three modes displayin...Competition mechanism in multiple four-wave mixing （MFWM） processes is demonstrated theoretically. Provided considering only two waves injected into a highly nonlinear fiber （HNLF）, there are three modes displaying comprehensive dynamic behaviors, such as fixed points, periodic motion, and chaotic motion. Especially, Mode C of MFWM is emphasized by analyzing its phase-space trajectory to demonstrate nonlinear wave- wave interactions. The study shows that, when the phase- space trajectory approaches or gets through a saddle point, a dramatic power depletion for the injected wave can be realized, with the representative point moving chaotically, but when phase-space trajectories are distributed around a center point, the power for the injected wave is retained almost invariable, with the representative point moving periodically. Finally, the evolvement of satellite wave over an optical fiber is investigated by comparing it with the interference pattern in Young＇s double-slit experiment.展开更多
文摘密度峰值聚类(density peaks clustering,DPC)是一种基于密度的聚类算法,该算法可以直观地确定类簇数量,识别任意形状的类簇,并且自动检测、排除异常点.然而,DPC仍存在些许不足:一方面,DPC算法仅考虑全局分布,在类簇密度差距较大的数据集聚类效果较差;另一方面,DPC中点的分配策略容易导致“多米诺效应”.为此,基于代表点(representative points)与K近邻(K-nearest neighbors,KNN)提出了RKNN-DPC算法.首先,构造了K近邻密度,再引入代表点刻画样本的全局分布,提出了新的局部密度;然后,利用样本的K近邻信息,提出一种加权的K近邻分配策略以缓解“多米诺效应”;最后,在人工数据集和真实数据集上与5种聚类算法进行了对比实验,实验结果表明,所提出的RKNN-DPC可以更准确地识别类簇中心并且获得更好的聚类结果.
基金supported by the Science and Technology on Space Intelligent Control Laboratory for National Defense(KGJZDSYS-2018-08)。
文摘Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.
文摘针对传统K近邻分类器在大规模数据集中存在时间和空间复杂度过高的问题,可采取原型选择的方法进行处理,即从原始数据集中挑选出代表原型(样例)进行K近邻分类而不降低其分类准确率.本文在CURE聚类算法的基础上,针对CURE的噪声点不易确定及代表点分散性差的特点,利用共享邻居密度度量给出了一种去噪方法和使用最大最小距离选取代表点进行改进,从而提出了一种新的原型选择算法PSCURE (improved prototype selection algorithm based on CURE algorithm).基于UCI数据集进行实验,结果表明:提出的PSCURE原型选择算法与相关原型算法相比,不仅能筛选出较少的原型,而且可获得较高的分类准确率.
文摘Competition mechanism in multiple four-wave mixing （MFWM） processes is demonstrated theoretically. Provided considering only two waves injected into a highly nonlinear fiber （HNLF）, there are three modes displaying comprehensive dynamic behaviors, such as fixed points, periodic motion, and chaotic motion. Especially, Mode C of MFWM is emphasized by analyzing its phase-space trajectory to demonstrate nonlinear wave- wave interactions. The study shows that, when the phase- space trajectory approaches or gets through a saddle point, a dramatic power depletion for the injected wave can be realized, with the representative point moving chaotically, but when phase-space trajectories are distributed around a center point, the power for the injected wave is retained almost invariable, with the representative point moving periodically. Finally, the evolvement of satellite wave over an optical fiber is investigated by comparing it with the interference pattern in Young＇s double-slit experiment.
