Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Natural gradient works efficiently in learning
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of entropy estimators for blind source separation
Signal Processing
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
Bayes Optimality in Linear Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
KPCA for semantic object extraction in images
Pattern Recognition
Binary sparse nonnegative matrix factorization
IEEE Transactions on Circuits and Systems for Video Technology
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Deterministic Column-Based Matrix Decomposition
IEEE Transactions on Knowledge and Data Engineering
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Generalized information potential criterion for adaptive system training
IEEE Transactions on Neural Networks
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We present a new linear discriminant analysis method based on information theory, where the mutual information between linearly transformed input data and the class labels is maximized. First, we introduce a kernel-based estimate of mutual information with a variable kernel size. Furthermore, we devise a learning algorithm that maximizes the mutual information w.r.t. the linear transformation. Two experiments are conducted: the first one uses a toy problem to visualize and compare the transformation vectors in the original input space; the second one evaluates the performance of the method for classification by employing cross-validation tests on four datasets from the UCI repository. Various classifiers are investigated. Our results show that this method can significantly boost class separability over conventional methods, especially for nonlinear classification.