影响因子：0.0
DOI码：10.3969/j.issn.1005-3085.2017.06.001
发表刊物：Chinese Journal of Engineering Mathematics
关键字：principle component analysis for tensors weighted-CUSUM; compression theory for high-dimensional information; Tucker decomposition; robust PCA; sparse PCA
摘要：In this paper, we summarize the past, present of principle component analysis for tensors in the context of information compression, and show some untouched research fields. Firstly, we review the conception of tensors and tensor decomposition which can be expressed by an unified statistical model. Secondly, by the order of the classical principal component analysis, robust principal component analysis and sparse principal component analysis, we summarize the development of relative statistical theories and algorithms where each one can be further divided into vector data, matrix data and tensor data from the simple to the complex.
论文类型：期刊论文
论文编号：1005-3085 (2017) 06-0571-20
卷号：34
期号：6
页面范围：571-590
ISSN号：1005-3085
是否译文：否
第一作者：Zhiming Xia