TY - GEN
T1 - Learning modewise independent components from tensor data using multilinear mixing model
AU - LU, Haiping
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Independent component analysis (ICA) is a popular unsupervised learning method. This paper extends it to multilinear modewise ICA (MMICA) for tensors and explores two architectures in learning and recognition. MMICA models tensor data as mixtures generated from modewise source matrices that encode statistically independent information. Its sources have more compact representations than the sources in ICA. We embed ICA into the multilinear principal component analysis framework to solve for each source matrix alternatively with a few iterations. Then we obtain mixing tensors through regularized inverses of the source matrices. Simulations on synthetic data show that MMICA can estimate hidden sources accurately from structured tensor data. Moreover, in face recognition experiments, it outperforms competing solutions with both architectures.
AB - Independent component analysis (ICA) is a popular unsupervised learning method. This paper extends it to multilinear modewise ICA (MMICA) for tensors and explores two architectures in learning and recognition. MMICA models tensor data as mixtures generated from modewise source matrices that encode statistically independent information. Its sources have more compact representations than the sources in ICA. We embed ICA into the multilinear principal component analysis framework to solve for each source matrix alternatively with a few iterations. Then we obtain mixing tensors through regularized inverses of the source matrices. Simulations on synthetic data show that MMICA can estimate hidden sources accurately from structured tensor data. Moreover, in face recognition experiments, it outperforms competing solutions with both architectures.
KW - independent component analysis
KW - mixing model
KW - multilinear subspace learning
KW - tensor
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=84886495492&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40991-2_19
DO - 10.1007/978-3-642-40991-2_19
M3 - Conference proceeding
AN - SCOPUS:84886495492
SN - 9783642409905
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 288
EP - 303
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Proceedings
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013
Y2 - 23 September 2013 through 27 September 2013
ER -