Fast Solution in Sparse LDA for Binary Classification

Special properties of binary classification and greedy algorithms enable speedup.

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.