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

Chen, S., Hong, X. 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.

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To link to this item 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)
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:14630
Uncontrolled Keywords:LEAST-SQUARES, CONSTRUCTION
Publisher:IEEE

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