Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

Chen, S., Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 and Harris, C.J. (2008) Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design. In: International Joint Conference on Neural Networks 2008 (IJCNN), Hong Kong, China. doi: 10.1109/IJCNN.2008.4633758

Abstract/Summary

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/14630
Item Type	Conference or Workshop Item
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	LEAST-SQUARES, CONSTRUCTION
Publisher	IEEE
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