Introduction to algorithms
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, GE
Combination of Face Classifiers for Person Identification
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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A new approach for optimal selection of dimensionality reduction methods for individual classifiers within a multiple classifier system is introduced for the face recognition problem. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are used as the appearance-based statistical methods for dimensionality reduction. A face is partitioned into five segments and each segment is processed by a particular dimensionality reduction method. This results in a low-complexity divide-and-conquer approach, implemented as a multiple-classifier system where distance-based individual classifiers are built using appearance-based statistical methods. The decisions of individual classifiers are unified by an appropriate combination method. Genetic Algorithms (GAs) are used to select the optimal dimensionality reduction method for each individual classifier. Experiments are conducted to show that the proposed approach outperforms the holistic methods.