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An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis.
Yang, Hannan; Lin, D Y; Li, Quefeng.
Afiliação
  • Yang H; Department of Biostatistics, University of North Carolina, Chapel Hill.
  • Lin DY; Department of Biostatistics, University of North Carolina, Chapel Hill.
  • Li Q; Department of Biostatistics, University of North Carolina, Chapel Hill.
Stat Sin ; 33(SI): 1343-1364, 2023 May.
Article em En | MEDLINE | ID: mdl-37455685
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 establish theoretical guarantee of its statistical properties in terms of variable selection and error rate consistency; in addition, we provide an explicit interpretation of the extra information brought by an additional feature in a LDA problem under some mild distributional assumptions. We demonstrate that this new algorithm drastically improves computational speed compared with other high-dimensional LDA methods, while maintaining comparable or even better classification performance.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Stat Sin Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Stat Sin Ano de publicação: 2023 Tipo de documento: Article