针对小样本问题,提出了一种基于QR分解的线性图嵌入(Linear Extension of Graph Embedding,LGE)求解算法,并将其用于人脸识别。与传统的用主成分分析进行降维不同,新算法利用QR分解对数据进行降维,然后在降维后的空间利用线性图嵌入算...针对小样本问题,提出了一种基于QR分解的线性图嵌入(Linear Extension of Graph Embedding,LGE)求解算法,并将其用于人脸识别。与传统的用主成分分析进行降维不同,新算法利用QR分解对数据进行降维,然后在降维后的空间利用线性图嵌入算法进行二次特征抽取,最后利用最近邻分类器进行分类识别。新算法有效的解决了小样本问题,并且在降维的过程中不损失鉴别信息,提高了算法的识别率。在Yale和PIE人脸数据库的实验表明了本文算法在识别性能上优于传统算法。展开更多
The inverse problem analysis method provides an effective way for the structural parameter identification.However,uncertainties wildly exist in the practical engineering inverse problems.Due to the coupling of multi-s...The inverse problem analysis method provides an effective way for the structural parameter identification.However,uncertainties wildly exist in the practical engineering inverse problems.Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters,the traditional inverse method under the deterministic framework faces the challenges in solving mechanism and computing cost.In this paper,an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameters according to the uncertain measured responses and modeling parameters.Firstly,the polygonal convex set model is established to quantify the epistemic uncertainties of modeling parameters.Afterwards,a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty.The transformed interval inverse problem involves the two-layer solving process including interval propagation and optimization updating.In order to solve the interval inverse problems considering response uncertainty,an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed.Through the coupling of the above two strategies,the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure,and then effectively realizes the uncertainty identification of unknown structural parameters.Finally,two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.展开更多
文摘针对小样本问题,提出了一种基于QR分解的线性图嵌入(Linear Extension of Graph Embedding,LGE)求解算法,并将其用于人脸识别。与传统的用主成分分析进行降维不同,新算法利用QR分解对数据进行降维,然后在降维后的空间利用线性图嵌入算法进行二次特征抽取,最后利用最近邻分类器进行分类识别。新算法有效的解决了小样本问题,并且在降维的过程中不损失鉴别信息,提高了算法的识别率。在Yale和PIE人脸数据库的实验表明了本文算法在识别性能上优于传统算法。
基金National Science Foundation of China(Grant No.51975199)the Changsha Municipal Natural Science Foundation(Grant No.kq2014050).
文摘The inverse problem analysis method provides an effective way for the structural parameter identification.However,uncertainties wildly exist in the practical engineering inverse problems.Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters,the traditional inverse method under the deterministic framework faces the challenges in solving mechanism and computing cost.In this paper,an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameters according to the uncertain measured responses and modeling parameters.Firstly,the polygonal convex set model is established to quantify the epistemic uncertainties of modeling parameters.Afterwards,a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty.The transformed interval inverse problem involves the two-layer solving process including interval propagation and optimization updating.In order to solve the interval inverse problems considering response uncertainty,an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed.Through the coupling of the above two strategies,the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure,and then effectively realizes the uncertainty identification of unknown structural parameters.Finally,two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.
