Technical Report CS-2006-04

Title: Linear Dimensionality Reduction for Classification
Authors: Ilya Blayvas, Michael Elad, and Ron Kimmel
Abstract: We consider the problem of dimensionality reduction for the sake of classification. Dimensionality reduction can become a crucial step in classification of high-dimensional data. Usually, the number of training points is limited and can even be smaller than the dimensionality of the data. We show that straightforward pursuing of optimality criteria can lead to `overfitting' and suboptimal performance. We show how regularization can be introduced to obtain better separation between classes in the reduced space and higher classification performance.
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