NDLI: Learning subspace kernels for classification

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Learning subspace kernels for classification

Content Provider	ACM Digital Library
Author	Ceran, Betul Chen, Jianhui Ji, Shuiwang Li, Qi Wu, Mingrui Ye, Jieping
Abstract	Kernel methods have been applied successfully in many data mining tasks. Subspace kernel learning was recently proposed to discover an effective low-dimensional subspace of a kernel feature space for improved classification. In this paper, we propose to construct a subspace kernel using the Hilbert-Schmidt Independence Criterion (HSIC). We show that the optimal subspace kernel can be obtained efficiently by solving an eigenvalue problem. One limitation of the existing subspace kernel learning formulations is that the kernel learning and classification are independent and the subspace kernel may not be optimally adapted for classification. To overcome this limitation, we propose a joint optimization framework, in which we learn the subspace kernel and subsequent classifiers simultaneously. In addition, we propose a novel learning formulation that extracts an uncorrelated subspace kernel to reduce the redundant information in a subspace kernel. Following the idea from multiple kernel learning, we extend the proposed formulations to the case when multiple kernels are available and need to be combined. We show that the integration of subspace kernels can be formulated as a semidefinite program (SDP) which is computationally expensive. To improve the efficiency of the SDP formulation, we propose an equivalent semi-infinite linear program (SILP) formulation which can be solved efficiently by the column generation technique. Experimental results on a collection of benchmark data sets demonstrate the effectiveness of the proposed algorithms.
Starting Page	106
Ending Page	114
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401908
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Support vector machines Hilbert-schmidt independence criterion Classification Subspace kernel
Content Type	Text
Resource Type	Article

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