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    Coordinate Descent for Mixed-norm NMF


    Potluru, Vamsi K. and Le Roux, Jonathan and Pearlmutter, Barak A. and Hershey, John R. and Brand, Matthew E. (2013) Coordinate Descent for Mixed-norm NMF. Technical Report. UNSPECIFIED.

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    Abstract

    Nonnegative matrix factorization (NMF) is widely used in a variety of machine learning tasks involving speech, documents and images. Being able to specify the structure of the matrix factors is crucial in incorporating prior information. The factors correspond to the feature matrix and the learnt representation. In particular, we allow an user-friendly specification of sparsity on the groups of features using the L1/L2 measure. Also, we propose a pairwise coordinate descent algorithm to minimize the objective. Experimental evidence of the efficacy of this approach is provided on the ORL faces dataset.

    Item Type: Monograph (Technical Report)
    Additional Information: This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
    Keywords: Coordinate Descent; Mixed-norm NMF; Nonnegative matrix factorization; machine learning;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 6553
    Identification Number: TR2013-130
    Depositing User: Barak Pearlmutter
    Date Deposited: 10 Nov 2015 16:43
    URI:

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