Sufficient Dimension Reduction via Principal L q Support Vector Machine ∗
نویسندگان
چکیده
منابع مشابه
Principal weighted support vector machines for sufficient dimension reduction in binary classification
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...
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