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    Intrinsic dimension estimation via nearest constrained subspace classifier

    Liao, L. and Zhang, Y. and Maybank, Stephen J. and Liu, Z. (2014) Intrinsic dimension estimation via nearest constrained subspace classifier. Pattern Recognition 47 (3), pp. 1485-1493. ISSN 0031-3203.

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    Abstract

    We consider the problems of classification and intrinsic dimension estimation on image data. A new subspace based classifier is proposed for supervised classification or intrinsic dimension estimation. The distribution of the data in each class is modeled by a union of of a finite number of affine subspaces of the feature space. The affine subspaces have a common dimension, which is assumed to be much less than the dimension of the feature space. The subspaces are found using regression based on the ℓ0-norm. The proposed method is a generalisation of classical NN (Nearest Neighbor), NFL (Nearest Feature Line) classifiers and has a close relationship to NS (Nearest Subspace) classifier. The proposed classifier with an accurately estimated dimension parameter generally outperforms its competitors in terms of classification accuracy. We also propose a fast version of the classifier using a neighborhood representation to reduce its computational complexity. Experiments on publicly available datasets corroborate these claims.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Intrinsic dimension estimation, Nearest constrained subspace classifier, Image classification, Sparse representation
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 02 Sep 2013 08:31
    Last Modified: 11 Apr 2014 10:23
    URI: http://eprints.bbk.ac.uk/id/eprint/8067

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