On Optimal Pairwise Linear Classifiers for Normal Distributions: The Two-Dimensional Case
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Pattern Classification (2nd Edition)
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Pattern Recognition Letters
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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A new linear dimensionality reduction technique based on chernoff distance
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Linear dimensionality reduction (LDR) techniques are quite important in pattern recognition due to their linear time complexity and simplicity. In this paper, we present a novel LDR technique which, though linear, aims to maximize the Chernoff distance in the transformed space; thus, augmenting the class separability in such a space. We present the corresponding criterion, which is maximized via a gradient-based algorithm, and provide convergence and initialization proofs. We have performed a comprehensive performance analysis of our method combined with two well-known classifiers, linear and quadratic, on synthetic and real-life data, and compared it with other LDR techniques. The results on synthetic and standard real-life data sets show that the proposed criterion outperforms the latter when combined with both linear and quadratic classifiers.