Multiple Kernel Spectral Regression for Dimensionality Reduction

Bing Liu, Shixiong Xia and Yong Zhou

Journal of Applied Mathematics, 2013, vol. 2013, 1-8

Abstract:

Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR) solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL) into SR for dimensionality reduction. The proposed approach (termed MKL-SR) seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS) induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR) further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljam:427462

DOI: 10.1155/2013/427462

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