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Probability density function estimation using orthogonal forward regression

Chen, S., Hong, X. and Harris, C.J. (2007) Probability density function estimation using orthogonal forward regression. In: International Joint Conference on Neural Networks (IJCNN 2007), Orlando, USA, https://doi.org/10.1109/IJCNN.2007.4371350 .

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To link to this item DOI: 10.1109/IJCNN.2007.4371350

Abstract/Summary

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.

Item Type:Conference or Workshop Item (Paper)
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:14628
Uncontrolled Keywords:LEAST-SQUARES, LOCAL REGULARIZATION, IDENTIFICATION
Publisher:IEEE

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