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Optimizing conflicting objectives in NMF using Pareto simulated annealing
Date Issued
2010-08-30
Date Available
2011-01-20T14:58:52Z
Abstract
Non-Negative matrix factorization (NMF) has emerged as an important technique for simplifying high-dimension data into interpretable factors. NMF has the attractive characteristic that the factor matrices are naturally sparse, thus allowing them to be readily interpreted. However, there is a tension between the accuracy of the factorization and the sparseness – it is the management of the trade-off between these two criteria that is the subject of this paper. We introduce a multi-criteria Simulated annealing framework that produces a Pareto set of solutions, which are non-dominated on both criteria. We show that solutions at one end of the Pareto front of solutions correspond to NMF factorizations produced with conventional optimization techniques, while solutions at the other end exhibit enhanced sparseness. Clustering is no longer to be observed either in the raw-data form of the matrix, or the generated heat-map form.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Subject – LCSH
Machine learning
Cluster analysis
Non-negative matrices
Simulated annealing (Mathematics)
Language
English
Status of Item
Peer reviewed
Conference Details
Paper presented at the 21st National Conference on Artificial Intelligence and Cognitive Science (AICS 2010), Galway, Ireland, 30 August - 1 September, 2010
This item is made available under a Creative Commons License
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