Max-margin semi-NMF

Conference paper

Kumar, B., Kotsia, I. and Patras, I. 2011. Max-margin semi-NMF. The 22nd British Machine Vision Conference (BMVC 2011). University of Dundee 29 Aug - 02 Sep 2011
TypeConference paper
TitleMax-margin semi-NMF
AuthorsKumar, B., Kotsia, I. and Patras, I.

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.

Research GroupResearch Group on Development of Intelligent Environments
ConferenceThe 22nd British Machine Vision Conference (BMVC 2011)
Publication process dates
Deposited27 Nov 2012
Output statusPublished
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.

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