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Sparse Multinomial Kernel Discriminant Analysis (sMKDA)

Sparse Multinomial Kernel Discriminant Analysis (sMKDA)
Sparse Multinomial Kernel Discriminant Analysis (sMKDA)
Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets.
linear discriminant analysis, kernel discriminant analysis, multi-class, multinomial, least-squares, optimal scaling, sparsity control
0031-3203
1795-1802
Harrison, Robert F.
c3ce2e0f-5408-4db9-90bd-a3149a932a72
Pasupa, Kitsuchart
952ededb-8c97-41b7-a65b-6aba31de2669
Harrison, Robert F.
c3ce2e0f-5408-4db9-90bd-a3149a932a72
Pasupa, Kitsuchart
952ededb-8c97-41b7-a65b-6aba31de2669

Harrison, Robert F. and Pasupa, Kitsuchart (2009) Sparse Multinomial Kernel Discriminant Analysis (sMKDA). Pattern Recognition, 42 (9), 1795-1802. (doi:10.1016/j.patcog.2009.01.025).

Record type: Article

Abstract

Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets.

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More information

Published date: 1 September 2009
Keywords: linear discriminant analysis, kernel discriminant analysis, multi-class, multinomial, least-squares, optimal scaling, sparsity control
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 267062
URI: https://eprints.soton.ac.uk/id/eprint/267062
ISSN: 0031-3203
PURE UUID: e19cdcf7-8e20-4d5a-be02-7a37fa992921

Catalogue record

Date deposited: 27 Jan 2009 14:25
Last modified: 18 Jul 2017 07:08

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