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    Supervised classification via constrained subspace and tensor sparse representation

    Liao, L. and Maybank, Stephen and Zhang, Y. and Liu, X. (2017) Supervised classification via constrained subspace and tensor sparse representation. In: UNSPECIFIED (ed.) Neural Networks (IJCNN), 2017 International Joint Conference on. IEEE Computer Society. ISBN 9781509061839.

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    Abstract

    SRC, a supervised classifier via sparse representation, has rapidly gained popularity in recent years and can be adapted to a wide range of applications based on the sparse solution of a linear system. First, we offer an intuitive geometric model called constrained subspace to explain the mechanism of SRC. The constrained subspace model connects the dots of NN, NFL, NS, NM. Then, inspired from the constrained subspace model, we extend SRC to its tensor-based variant, which takes as input samples of high-order tensors which are elements of an algebraic ring. A tensor sparse representation is used for query tensors. We verify in our experiments on several publicly available databases that the tensor-based SRC called tSRC outperforms traditional SRC in classification accuracy. Although demonstrated for image recognition, tSRC is easily adapted to other applications involving underdetermined linear systems.

    Metadata

    Item Type: Book Section
    Additional Information: The 2017 International Joint Conference on Neural Networks (IJCNN 2017) - Anchorage, Alaska, USA, May 14–19, 2017. (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. ISSN: 2161-4407
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 06 Feb 2017 11:05
    Last Modified: 26 Sep 2017 13:44
    URI: http://eprints.bbk.ac.uk/id/eprint/18087

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