Non-negative Matrix Factorization with Schatten p-norms Reguralization

In this paper we study the effect of regularization on clustering results provided by Non-negative Matrix Factorization (NMF). Different kinds of regularization terms were previously added to the NMF objective function in order to produce sparser results and thus to obtain a more qualitative partiti...

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Bibliographic Details
Published inNeural Information Processing pp. 52 - 59
Main Authors Redko, Ievgen, Bennani, Younès
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Cluster Validation Index
Multivariate Density Estimation
Negative Matrix Factorization
Regularization Term
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Summary:In this paper we study the effect of regularization on clustering results provided by Non-negative Matrix Factorization (NMF). Different kinds of regularization terms were previously added to the NMF objective function in order to produce sparser results and thus to obtain a more qualitative partition of data. We would like to propose the general framework for regularized NMF based on Schatten p-norms. Experimental results show the effectiveness of our approach on different data sets.
ISBN:9783319126395
3319126393
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-12640-1_7