A single-class SVM based algorithm for computing an identifiable NMF

Slim Essid

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The geometric interpretation of Nonnegative Matrix Factorisation (NMF) as the problem of determining a convex cone that "well describes" the data under analysis has been key for addressing a major shortcoming of the "mainstream" NMF algorithms, that is the non-identifiability of the factorisation. On the basis of such geometric motivations, this paper proposes a novel algorithm that makes use of single-class support vector machines to recover the targeted NMF components. Not only does this new approach alleviate the NMF illposedness issue, but also it allows for automatically estimating the number of relevant NMF components, as demonstrated through experiments described in the paper. Moreover, it is readily kernelised thus opening the way for non-linear factorisations of the data.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages2053-2056
Number of pages4
DOIs
Publication statusPublished - 23 Oct 2012
Externally publishedYes
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period25/03/1230/03/12

Keywords

  • identifiability
  • nonnegative matrix factorisation
  • single-class support vector machines

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