Canonical kernel dimension reduction

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• 作者：Chenyang Tao^a ; ^b ; Jianfeng Feng^a ; ^b ; ^c ; ^{jianfeng64@gmail.com}
• 关键词：Canonical correlation analysis ; Canonical functions ; Kernel dimension reduction ; Krylov subspace ; Sufficient dimension reduction ; Reproducing kernel Hilbert space
• 刊名：Computational Statistics & Data Analysis
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：107
• 期：Complete
• 页码：131-148
• 全文大小：665 K
• 卷排序：107

文摘

A new sufficient dimension reduction method based on kernel canonical functions. This new method is distribution free and highly scalable. We give theoretical proof of the sufficient dimension reduction property. We present efficient algorithms and discuss the choice of loss function. Extensive experiments demonstrate its advantage over existing approaches.