Matrix analysis
Kalman filtering theory
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Elements of information theory
Elements of information theory
Array Signal Processing: Concepts and Techniques
Array Signal Processing: Concepts and Techniques
Discriminative Features for Document Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An efficient discriminant-based solution for small sample size problem
Pattern Recognition
Robust Discriminant Analysis Based on Nonparametric Maximum Entropy
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Information-theoretic competitive and cooperative learning for self-organizing maps
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Information-theoretic approaches to SVM feature selection for metagenome read classification
Computational Biology and Chemistry
Radar HRRP recognition based on discriminant information analysis
WSEAS Transactions on Information Science and Applications
Super-class Discriminant Analysis: A novel solution for heteroscedasticity
Pattern Recognition Letters
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
Regularized discriminant entropy analysis
Pattern Recognition
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Using elementary information-theoretic tools, we develop a novel technique for linear transformation from the space of observations into a low-dimensional (feature) subspace for the purpose of classification. The technique is based on a numerical optimization of an information-theoretic objective function, which can be computed analytically. The advantages of the proposed method over several other techniques are discussed and the conditions under which the method reduces to linear discriminant analysis are given. We show that the novel objective function enjoys many of the properties of the mutual information and the Bayes error and we give sufficient conditions for the method to be Bayes-optimal. Since the objective function is maximized numerically, we show how the calculations can be accelerated to yield feasible solutions. The performance of the method compares favorably to other linear discriminant-based feature extraction methods on a number of simulated and real-world data sets.