Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Elements of information theory
Elements of information theory
The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Feature Extraction Using Information-Theoretic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Feature Extractors Based on Mutual Information
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Journal of Cognitive Neuroscience
Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose a novel method of linear feature extraction with info-margin maximization (InfoMargin) from information theoretic viewpoint. It aims to achieve a low generalization error by maximizing the information divergence between the distributions of different classes while minimizing the entropy of the distribution in each single class. We estimate the density of data in each class with Gaussian kernel Parzen window and develop an efficient and fast convergent algorithm to calculate quadratic entropy and divergence measure. Experimental results show that our method outperforms the traditional feature extraction methods in the classification and data visualization tasks.