Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A new approach to multi-class linear dimensionality reduction
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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Subclass Discriminant Analysis (SDA) [10] is a dimensionality reduction method that has been proven to be successful for different types of class distributions. The advantage of SDA is that since it does not treat the class-conditional distributions as uni-modal ones, the nonlinearly separable problems can be handled as linear ones. The problem with this strategy, however, is that to estimate the number of subclasses needed to represent the distribution of each class, i.e., to find out the best partition, all possible solutions should be verified. Therefore, this approach leads to an associated high computational cost. In this paper, we propose a method that optimizes the computational burden of SDA-based classification by simply reducing the number of classes to be examined through choosing a few classes of the training set prior to the execution of SDA. To select the classes to be partitioned, the intra-set distance is employed as a criterion and a k-means clustering is performed to divide them. Our experimental results for an artificial data set and two face databases demonstrate that the processing CPU-time of the optimized SDA could be reduced dramaticallywithout sacrificing either the classification accuracy or the computational complexity.