Block-Iterative Algorithms for Non-negative Matrix Approximation

In this paper we present new algorithms for non-negative matrix approximation (NMA), commonly known as the NMF problem. Our methods improve upon the well-known methods of Lee & Seung [12] for both the Frobenius norm as well the Kullback-Leibler divergence versions of the problem. For the latter...

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Bibliographic Details

Published in:	2008 Eighth IEEE International Conference on Data Mining pp. 1037 - 1042
Main Author:	Sra, S.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2008
Subjects:	Acceleration Approximation algorithms approximations Biomedical imaging block-iterative algorithms Bregman divergence Data analysis Data mining Linear algebra low-rank approximation Matrix decomposition Minimization methods Nonnegative matrix factorization Vectors
ISBN:	076953502X, 9780769535029
ISSN:	1550-4786
Online Access:	Get full text
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