Nonparametric Dimension Reduction via Maximizing Pairwise Separation Probability

IEEE transactions on neural networks and learning systems, Volume 30, Issue 10, 2019, Pages 1-6.

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Abstract:

In this brief, we propose a novel nonparametric supervised linear dimension reduction (SLDR) algorithm that extracts the features by maximizing the pairwise separation probability. The separation probability, as a new class separability measure, describes the generalization accuracy when we use the obtained features to train a linear clas...More

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