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Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization
Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization
The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favourably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.
1083-4419
1708-1717
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, S., Hong, X. and Harris, C.J. (2004) Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34 (4), 1708-1717.

Record type: Article

Abstract

The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favourably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

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More information

Published date: August 2004
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 259420
URI: https://eprints.soton.ac.uk/id/eprint/259420
ISSN: 1083-4419
PURE UUID: d06894b9-2a7e-4f14-8393-7a0e6b2c0418

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Date deposited: 07 Jun 2004
Last modified: 12 Sep 2017 16:33

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