Support vector density estimation
Advances in kernel methods
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Multivariate Density Estimation: an SVM Approach
Multivariate Density Estimation: an SVM Approach
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Neural Networks
Tailoring density estimation via reproducing kernel moment matching
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Construction of tunable radial basis function networks using orthogonal forward selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Robust Bayesian mixture modelling
Neurocomputing
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
Orthogonal forward selection for constructing the radial basis function network with tunable nodes
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Fast orthogonal least squares algorithm for efficient subset modelselection
IEEE Transactions on Signal Processing
Adaptive minimum-BER linear multiuser detection for DS-CDMA signalsin multipath channels
IEEE Transactions on Signal Processing
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
Robust nonlinear model identification methods using forward regression
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Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear model structure detection using optimum experimental design and orthogonal least squares
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.