Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Feature Extraction Using Information-Theoretic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Sparse Nonlinear Bayesian Online Kernel Regression
ADVCOMP '08 Proceedings of the 2008 The Second International Conference on Advanced Engineering Computing and Applications in Sciences
Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression
Expert Systems with Applications: An International Journal
Feature selection with dynamic mutual information
Pattern Recognition
KNN-kernel density-based clustering for high-dimensional multivariate data
Computational Statistics & Data Analysis
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Sparse Bayesian modeling with adaptive kernel learning
IEEE Transactions on Neural Networks
Effective feature selection scheme using mutual information
Neurocomputing
Kernel bandwidth optimization in spike rate estimation
Journal of Computational Neuroscience
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
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
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Graph embedding based feature selection
Neurocomputing
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A novel sparse kernel density estimation method is proposed based on the sparse Bayesian learning with random iterative dictionary preprocessing. Using empirical cumulative distribution function as the response vectors, the sparse weights of density estimation are estimated by sparse Bayesian learning. The proposed iterative dictionary learning algorithm is used to reduce the number of kernel computations, which is an essential step of the sparse Bayesian learning. With the sparse kernel density estimation, the quadratic Renyi entropy based normalized mutual information feature selection method is proposed. The simulation of three examples demonstrates that the proposed method is comparable to the typical Parzen kernel density estimations. And compared with other state-of-art sparse kernel density estimations, our method also has a shown very good performance as to the number of kernels required in density estimation. For the last example, the Friedman data and Housing data are used to show the property of the proposed feature variables selection method.