Block Iterative Algorithms for Non-negative Matrix Approximation

Sra, S

doi:10.1109/ICDM.2008.77

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Block Iterative Algorithms for Non-negative Matrix Approximation

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4781221
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Sra, S. (2009). Block Iterative Algorithms for Non-negative Matrix Approximation. In F. Giannotti, D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, & X. Wu (Eds.), 2008 Eighth IEEE International Conference on Data Mining (pp. 1037-1042). Piscataway, NJ, USA: IEEE.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C62B-8

Abstract

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~citelee00} for both the Frobenius norm as well the Kullback-Leibler divergence versions of the problem. For the latter problem, our results are especially interesting because it seems to have witnessed much lesser algorithmic progress as compared to the Frobenius norm NMA problem. Our algorithms are based on a particular textbf {block-iterative} acceleration technique for EM, which preserves the multiplicative nature of the updates and also ensures monotonicity. Furthermore, our algorithms also naturally apply to the Bregman-divergence NMA algorithms of~cite{suv.nips. Experimentally, we show that our algorithms outperform the traditional Lee/Seung approach most of the time.