Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A fast fixed-point algorithm for independent component analysis
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Image Feature Extraction by Sparse Coding and Independent Component Analysis
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Channel selection and feature projection for cognitive load estimation using ambulatory EEG
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
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In pattern recognition, a suitable criterion for feature selection is the mutual information (MI) between feature vectors and class labels. Estimating MI in high dimensional feature spaces is problematic in terms of computation load and accuracy. We propose an independent component analysis based MI estimation (ICA-MI) methodology for feature selection. This simplifies the high dimensional MI estimation problem into multiple one-dimensional MI estimation problems. Nonlinear ICA transformation is achieved using piecewise local linear approximation on partitions in the feature space, which allows the exploitation of the additivity property of entropy and the simplicity of linear ICA algorithms. Number of partitions controls the tradeoff between more accurate approximation of the nonlinear data topology and small-sample statistical variations in estimation. We test the ICA-MI feature selection framework on synthetic, UCI repository, and EEG activity classification problems. Experiments demonstrate, as expected, that the selection of the number of partitions for local linear ICA is highly problem dependent and must be carried out properly through cross validation. When this is done properly, the proposed ICA-MI feature selection framework yields feature ranking results that are comparable to the optimal probability of error based feature ranking and selection strategy at a much lower computational load.