Multiclass Sparse Discriminant Analysis

TL;DR

This paper introduces a new multiclass sparse discriminant analysis method that estimates all discriminant directions simultaneously, with theoretical guarantees and superior performance demonstrated through experiments.

Contribution

The paper proposes a novel multiclass sparse discriminant analysis method with simultaneous estimation, theoretical validation, and efficient computation for high-dimensional data.

Findings

Method performs well on simulated data

Method outperforms existing approaches on real data

Theoretical guarantees established for variable selection and convergence

Abstract

In recent years many sparse linear discriminant analysis methods have been proposed for high-dimensional classification and variable selection. However, most of these proposals focus on binary classification and they are not directly applicable to multiclass classification problems. There are two sparse discriminant analysis methods that can handle multiclass classification problems, but their theoretical justifications remain unknown. In this paper, we propose a new multiclass sparse discriminant analysis method that estimates all discriminant directions simultaneously. We show that when applied to the binary case our proposal yields a classification direction that is equivalent to those by two successful binary sparse LDA methods in the literature. An efficient algorithm is developed for computing our method with high-dimensional data. Variable selection consistency and rates of convergence are established under the ultrahigh dimensionality setting. We further demonstrate the superior performance of our proposal over the existing methods on simulated and real data.