Bayes Error Estimation Using Parzen and k-NN Procedures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
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
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
Image covariance-based subspace method for face recognition
Pattern Recognition
On information and distance measures, error bounds, and feature selection
Information Sciences: an International Journal
Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern classification using composite features
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
IEEE Transactions on Image Processing
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Matrix representation in pattern classification
Expert Systems with Applications: An International Journal
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In this paper, we propose a new discriminant analysis using composite features for pattern classification. A composite feature consists of a number of primitive features, each of which corresponds to an input variable. The covariance of composite features is obtained from the inner product of composite features and can be considered as a generalized form of the covariance of primitive features. It contains information on statistical dependency among multiple primitive features. A discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (LDA). Unlike LDA, the number of extracted features can be larger than the number of classes in C-LDA, which is a desirable property especially for binary classification problems. Experimental results on several data sets indicate that C-LDA provides better classification results than other methods based on primitive features.