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.
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 Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Administrator |
| Date Deposited: | 02 Sep 2013 08:31 |
| Last Modified: | 09 Aug 2023 12:34 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/8067 |
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