Welcome to the InfoSci Platform
Reference Hub48
Swarm Intelligence for Non-Negative Matrix Factorization

Swarm Intelligence for Non-Negative Matrix Factorization

Andreas Janecek, Ying Tan
Copyright: © 2011 |Volume: 2 |Issue: 4 |Pages: 23
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781613509340|DOI: 10.4018/jsir.2011100102
Cite Article Cite Article

MLA

Janecek, Andreas, and Ying Tan. "Swarm Intelligence for Non-Negative Matrix Factorization." IJSIR vol.2, no.4 2011: pp.12-34. http://doi.org/10.4018/jsir.2011100102

APA

Janecek, A. & Tan, Y. (2011). Swarm Intelligence for Non-Negative Matrix Factorization. International Journal of Swarm Intelligence Research (IJSIR), 2(4), 12-34. http://doi.org/10.4018/jsir.2011100102

Chicago

Janecek, Andreas, and Ying Tan. "Swarm Intelligence for Non-Negative Matrix Factorization," International Journal of Swarm Intelligence Research (IJSIR) 2, no.4: 12-34. http://doi.org/10.4018/jsir.2011100102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article presents efforts to improve the convergence, approximation quality, and classification accuracy of NMF using five different meta-heuristics based on swarm intelligence. Several properties of the NMF objective function motivate the utilization of meta-heuristics: this function is non-convex, discontinuous, and may possess many local minima. The proposed optimization strategies are two-fold: On the one hand, a new initialization strategy for NMF is presented in order to initialize the NMF factors prior to the factorization; on the other hand, an iterative update strategy is proposed, which improves the accuracy per runtime for the multiplicative update NMF algorithm. The success of the proposed optimization strategies are shown by applying them on synthetic data and data sets coming from the areas of spam filtering/email classification, and evaluate them also in their application context. Experimental results show that both optimization strategies are able to improve NMF in terms of faster convergence, lower approximation error, and better classification accuracy. Especially the initialization strategy leads to significant reductions of the runtime per accuracy ratio for both, the NMF approximation as well as the classification results achieved with NMF.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.