文献类型：期刊文献

英文题名：Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data

作者：Zhou, Guoxu[1,2];Zhao, Qibin[2];Zhang, Yu[3];Adali, Tulay[4];Xie, Shengli[5];Cichocki, Andrzej[2,6,7]

机构：[1]Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China;[2]RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, Wako, Saitama 3510198, Japan;[3]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[4]Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Baltimore, MD 21250 USA;[5]Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China;[6]Skolkovo Inst Sci & Technol SKOLTECH, Moscow 143025, Russia;[7]Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland

年份：2016

卷号：104

期号：2

起止页码：310

外文期刊名：PROCEEDINGS OF THE IEEE

收录：;EI(收录号：20160201782704);WOS:【SCI-EXPANDED(收录号:WOS:000368980100007)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grants U1201253, 61202155, and 61305028; by the Guangdong Natural Science Foundation under Grant 2014A030308009, and by the JSPS KAKENHI under Grants 26730125 and 15K15955.

语种：英文

外文关键词：Analysis of multirelational data; constrained Tucker decompositions for multiblock data; CP (CANDECOMP/PARAFAC) decompositions; data fusion; group and joint independent component analysis; independent vector analysis (IVA); (multilinear) independent component analysis; (multiway) blind source separation (BSS); nonnegative/sparse matrix/tensor factorizations

摘要：With the increasing availability of various sensor technologies, we now have access to large amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be jointly analyzed to explore their latent connections. Various component analysis methods have played an increasingly important role for the analysis of such coupled data. In this article, we first provide a brief review of existing matrix-based (two-way) component analysis methods for the joint analysis of such data with a focus on biomedical applications. Then, we discuss their important extensions and generalization to multiblock multiway (tensor) data. We show how constrained multiblock tensor decomposition methods are able to extract similar or statistically dependent common features that are shared by all blocks, by incorporating the multiway nature of data. Special emphasis is given to the flexible common and individual feature analysis of multiblock data with the aim to simultaneously extract common and individual latent components with desired properties and types of diversity. Illustrative examples are given to demonstrate their effectiveness for biomedical data analysis.