|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.|
|Copyright||The above paper is copyright by the Technion, Author(s), or others. Please contact the author(s) for more information|
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