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

Seung Jun Shin, Yichao Wu, Hao Helen Zhang and Yufeng Liu

Biometrika, 2017, vol. 104, issue 1, 67-81

Abstract: SUMMARY 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.

Keywords: Fisher consistency; Hyperplane alignment; Reproducing kernel Hilbert space; Weighted support vector machine (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (9)

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