Max-margin semi-NMF

Kumar, B.G.Vijay and Kotsia, Irene and Patras, Ioannis (2011) Max-margin semi-NMF. In: The 22nd British Machine Vision Conference (BMVC 2011), 29 August - 2 September 2011, University of Dundee.

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Official URL: http://dx.doi.org/10.5244/C.25.129

Abstract

In this paper, we propose a maximum-margin framework for classification using Non-negative Matrix Factorization. In contrast to previous approaches where the classification and matrix factorization are separated, we incorporate the maximum margin constraints within the NMF formuation i.e. we solve for a base matrix that maximizes the margin of the classifier in the low dimensional feature space. This results in a non-convex constrained optimization problem with respect to the bases, the projection coefficients and the separating hyperplane, which we propose to solve in an iterative way, solving at each iteration a set of convex sub-problems with respect to subsets of the unknown variables. The resulting basis matrix is used to extract features that maximize the margin of the resulting classifier. The performance of the proposed algorithm is evaluated on several publicly available datasets where it is shown to consistently outperform Discriminative NMF and SVM classifiers that use features extracted by semi-NMF.

Item Type:Conference or Workshop Item (Paper)
Additional Information:

In Jesse Hoey, Stephen McKenna and Emanuele Trucco, Proceedings of the British Machine Vision Conference, pp 129.1-129.11. BMVA Press, September 2011.

Research Areas:Middlesex University Schools and Centres > School of Science and Technology > Computer Science
Middlesex University Schools and Centres > School of Science and Technology > Computer Science > Intelligent Environments group
ID Code:9595
Deposited On:27 Nov 2012 06:25
Last Modified:30 Oct 2014 15:00

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