Probabilistic factorization of non-negative data with entropic co-occurrence constraints

Paris Smaragdis, Madhusudana Shashanka, Bhiksha Raj, Gautham J. Mysore

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper we present a probabilistic algorithm which fac- torizes non-negative data. We employ entropic priors to additionally satisfy that user specified pairs of factors in this model will have their cross entropy maximized or minimized. These priors allow us to construct factorization algorithms that result in maximally statistically different factors, something that generic non-negative factorization algorithms cannot explicitly guarantee. We further show how this approach can be used to discover clusters of factors which allow a richer description of data while still effectively performing a low rank analysis.

Original languageEnglish (US)
Pages (from-to)330-337
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5441
DOIs
StatePublished - 2009
Externally publishedYes
Event8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009 - Paraty, Brazil
Duration: Mar 15 2009Mar 18 2009

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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