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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chen, S. Hong, X. Harris, C.J. |
| Copyright Year | 2007 |
| Description | Author affiliation: Southampton Univ., Southampton (Chen, S.; Harris, C.J.) |
| Abstract | Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate. |
| Starting Page | 2492 |
| Ending Page | 2497 |
| File Size | 698493 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4371350 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Probability density function Kernel Support vector machines Distribution functions Testing Quadratic programming Neural networks Computational efficiency Mean square error methods Optimization methods |
| Content Type | Text |
| Resource Type | Article |
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