In this article, the computation of μ-values known as Structured Singular Values SSV for the companion matrices is presented. The comparison of lower bounds with the well-known MATLAB routine mussv is investigated. T...In this article, the computation of μ-values known as Structured Singular Values SSV for the companion matrices is presented. The comparison of lower bounds with the well-known MATLAB routine mussv is investigated. The Structured Singular Values provides important tools to analyze the stability and instability analysis of closed loop time invariant systems in the linear control theory as well as in structured eigenvalue perturbation theory.展开更多
For an arbitrary tensor(multi-index array) with linear constraints at each direction,it is proved that the factors of any minimal canonical tensor approximation to this tensor satisfy the same linear constraints for t...For an arbitrary tensor(multi-index array) with linear constraints at each direction,it is proved that the factors of any minimal canonical tensor approximation to this tensor satisfy the same linear constraints for the corresponding directions.展开更多
针对高维数据的特征提取问题,将广义低秩矩阵近似(GLRAM)与对角主成分分析(DialPCA)相结合,提出一种新的特征提取方法 GLRAM Plus DialPCA用于进行图像识别。通过广义低秩矩阵对原始图像进行近似,再做对角化变化,采用二维主成分分析(2DP...针对高维数据的特征提取问题,将广义低秩矩阵近似(GLRAM)与对角主成分分析(DialPCA)相结合,提出一种新的特征提取方法 GLRAM Plus DialPCA用于进行图像识别。通过广义低秩矩阵对原始图像进行近似,再做对角化变化,采用二维主成分分析(2DPCA)提取数据行列之间的相关性特征,并利用最近邻分类器计算图像识别率。基于FERET和ORL人脸数据库的实验结果表明,与单一的GLRAM或2DPCA相比,GLRAM Plus DialPCA在姿态、光照和表情变化的情况下识别率更高,特征提取速度更快。展开更多
文摘In this article, the computation of μ-values known as Structured Singular Values SSV for the companion matrices is presented. The comparison of lower bounds with the well-known MATLAB routine mussv is investigated. The Structured Singular Values provides important tools to analyze the stability and instability analysis of closed loop time invariant systems in the linear control theory as well as in structured eigenvalue perturbation theory.
基金supported by the Russian Fund for Basic Research (RFBR grant 08-01-00115,RFBR/DFG grant 09-01-91332,RFBR grant 09-01-12058)Priority Research Programme of Department of Mathematical Sciences of Russian Academy of Sciences
文摘For an arbitrary tensor(multi-index array) with linear constraints at each direction,it is proved that the factors of any minimal canonical tensor approximation to this tensor satisfy the same linear constraints for the corresponding directions.
文摘针对高维数据的特征提取问题,将广义低秩矩阵近似(GLRAM)与对角主成分分析(DialPCA)相结合,提出一种新的特征提取方法 GLRAM Plus DialPCA用于进行图像识别。通过广义低秩矩阵对原始图像进行近似,再做对角化变化,采用二维主成分分析(2DPCA)提取数据行列之间的相关性特征,并利用最近邻分类器计算图像识别率。基于FERET和ORL人脸数据库的实验结果表明,与单一的GLRAM或2DPCA相比,GLRAM Plus DialPCA在姿态、光照和表情变化的情况下识别率更高,特征提取速度更快。
