Neural Computation
Support vector density estimation
Advances in kernel methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Local overfitting control via leverages
Neural Computation
Multivariate Density Estimation: an SVM Approach
Multivariate Density Estimation: an SVM Approach
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft clustering for nonparametric probability density function estimation
Pattern Recognition Letters
Neurocomputing
Sparse kernel modelling: a unified approach
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Relevance units latent variable model and nonlinear dimensionality reduction
IEEE Transactions on Neural Networks
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
Probability density estimation with tunable kernels using orthogonal forward regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Probability density estimation based on nonparametric local kernel regression
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Dependence tree structure estimation via copula
International Journal of Automation and Computing
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Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test 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 of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKD estimate with comparable accuracy to that of the full-sample optimised PW density estimate.