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• W2141583337 abstract "In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The manifold regularization improves the standard SIR in two aspects: 1) it encodes the local geometry for SIR and 2) it enables SIR to deal with transductive and semi-supervised learning problems. We prove that the proposed graph Laplacian based regularization is convergent at rate root-n. The projection directions of the regularized SIR are optimized by using a conjugate gradient method on the Grassmann manifold. Experimental results support our theory." @default.
• W2141583337 created "2016-06-24" @default.
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• W2141583337 date "2009-12-07" @default.
• W2141583337 modified "2023-10-18" @default.
• W2141583337 title "Manifold Regularization for SIR with Rate Root-n Convergence" @default.
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