Max-margin non-negative matrix factorization

Kumar, B.G.Vijay, Kotsia, Irene ORCID logoORCID: and Patras, Ioannis (2012) Max-margin non-negative matrix factorization. Image and Vision Computing, 30 (4-5) . pp. 279-291. ISSN 0262-8856 [Article]


In this paper we introduce a supervised, maximum margin framework for linear and non-linear Non-negative Matrix Factorization. By contrast to existing methods in which the matrix factorization phase (i.e. the feature extraction phase) and the classification phase are separated, we incorporate the maximum margin classification constraints within the NMF formulation. This results to a non-convex constrained optimization problem with respect to the bases and the separating hyperplane, which we solve following a block coordinate descent iterative optimization procedure. At each iteration a set of convex (constrained quadratic or Support Vector Machine-type) sub-problems are solved with respect to subsets of the unknown variables. By doing so, we obtain a bases matrix that maximizes the margin of the classifier in the low dimensional space (in the linear case) or in the high dimensional feature space (in the non-linear case). The proposed algorithms are evaluated on several computer vision problems such as pedestrian detection, image retrieval, facial expression recognition and action recognition where they are shown to consistently outperform schemes that extract features using bases that are learned using semi-NMF and classify them using an SVM classifier.

Item Type: Article
Keywords (uncontrolled): Non-negative Matrix Factorization; Supervised feature extraction; Semi-NMF; Max-margin classifier
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Intelligent Environments group
Item ID: 9661
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Depositing User: Devika Mohan
Date Deposited: 19 Dec 2012 06:51
Last Modified: 13 Oct 2016 14:25

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