An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis.

ABSTRACT: High-dimensional classification is an important statistical problem that has applications in many areas. One widely used classifier is the Linear Discriminant Analysis (LDA). In recent years, many regularized LDA classifiers have been proposed to solve the problem of high-dimensional classification. However, these methods rely on inverting a large matrix or solving large-scale optimization problems to render classification rules-methods that are computationally prohibitive when the dimension is ultra-high. With the emergence of big data, it is increasingly important to develop more efficient algorithms to solve the high-dimensional LDA problem. In this paper, we propose an efficient greedy search algorithm that depends solely on closed-form formulae to learn a high-dimensional LDA rule. We

SUBMITTER: Yang H 

PROVIDER: S-EPMC10348717 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature