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Principal weighted support vector machines for sufficient dimension reduction in binary classification.

ABSTRACT: Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are studied, and an efficient computing algorithm is proposed. Numerical examples demonstrate its performance in binary classification.

SUBMITTER: Shin SJ

PROVIDER: S-EPMC5793677 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Principal weighted support vector machines for sufficient dimension reduction in binary classification.

Shin Seung Jun SJ Wu Yichao Y Zhang Hao Helen HH Liu Yufeng Y

Biometrika 20170119 1

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are s ...[more]

PMID: 29430027

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