A column-wise update algorithm for nonnegative matrix factorization in Bregman divergence with an orthogonal constraint

Recently orthogonal nonnegative matrix factorization (ONMF), imposing an orthogonal constraint into NMF, has been attracting a great deal of attention. ONMF is more appropriate than standard NMF for a clustering task because the constrained matrix can be considered as an indicator matrix. Several it...

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Bibliographic Details
Published in:	Machine learning Vol. 103; no. 2; pp. 285 - 306
Main Authors:	Kimura, Keigo, Kudo, Mineichi, Tanaka, Yuzuru
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2016 Springer Nature B.V
Subjects:	Algorithms Approximation Artificial Intelligence Computer Science Control Convergence Divergence Factorization Mathematical analysis Matrix Mechatronics Natural Language Processing (NLP) Norms Robotics Simulation and Modeling Tasks Bregman Divergence Orthogonal Factorization Orthogonal nonnegative matrix factorization Column-wise Update
ISSN:	0885-6125, 1573-0565
Online Access:	Get full text
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