Linked Component Analysis from Matrices to High-Order Tensors: Applications to Biomedical Data

Proceedings of the IEEE

Volumn 104, Issue 2, 2016, Pages 310-331

  Zhou, Guoxu ^a,b   Zhao, Qibin ^b   Zhang, Yu ^c   Adali, Tulay ^d   Xie, Shengli ^a   Cichocki, Andrzej ^b,e,f  

^a GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^b RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^c School of Information Science and Engineering   (China)

^d Baltimore County   (United States)

^e SKOLKOVO INSTITUTE OF SCIENCE AND TECHNOLOGY   (Russian Federation)

^f SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

(multilinear) independent component analysis; (multiway) blind source separation (BSS); 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); nonnegative sparse matrix tensor factorizations

Indexed keywords

BLIND SOURCE SEPARATION; DATA FUSION; DATA MINING; DECOMPOSITION; INDEPENDENT COMPONENT ANALYSIS; MEDICAL APPLICATIONS; TENSORS;

ANALYSIS OF MULTIRELATIONAL DATA; CANDECOMP; INDEPENDENT VECTOR ANALYSIS; MULTI BLOCKS; NON NEGATIVES;

MATRIX ALGEBRA;

EID: 84953282331     PISSN: 00189219     EISSN: 15582256     Source Type: Journal    
DOI: 10.1109/JPROC.2015.2474704     Document Type: Review

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