Adaptive graph regularized Nonnegative Matrix Factorization via feature selection

Jing Yan Wang*, Islam Almasri, Xin Gao

*Corresponding author for this work

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

46 Scopus citations

Abstract

Nonnegative Matrix Factorization (NMF), a popular compact data representation method, fails to discover the intrinsic geometrical structure of the data space. Graph regularized NMF (GrNMF) is proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data feature space. However using the original feature space directly is not appropriate because of the noisy and irrelevant features. In this paper, we propose a novel data representation algorithm by integrating feature selection and graph regularization for NMF. Instead of using a fixed graph as GrNMF, we regularize NMF with an adaptive graph constructed according to the feature selection results. A uniform object is built to consider feature selection, NMF and adaptive graph regularization jointly, and a novel algorithm is developed to update the graph, feature weights and factorization parameters iteratively. Data clustering experiment shows the efficacy of the proposed method on the Yale database.

Original languageEnglish (US)
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages963-966
Number of pages4
StatePublished - 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1211/15/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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