Magazine Article

Fast Solution in Sparse LDA for Binary Classification

TBMG-7969

05/01/2010

Abstract
Content

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable-selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bioinformatics. Because of its combinatorial nature, feature- or variable-selection problems are “NP-hard” or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms.

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Citation
"Fast Solution in Sparse LDA for Binary Classification," Mobility Engineering, May 1, 2010.
Additional Details
Publisher
Published
May 1, 2010
Product Code
TBMG-7969
Content Type
Magazine Article
Language
English