2008 LearningSubspaceKernelsforClass

• (Chen et al., 2008) ⇒ Jianhui Chen, Shuiwang Ji, Betul Ceran, Qi Li, Mingrui Wu, and Jieping Ye. (2008). “Learning Subspace Kernels for Classification.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401908

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• http://scholar.google.com/scholar?q=%22Learning+subspace+kernels+for+classification%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401908&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 LearningSubspaceKernelsforClass	Shuiwang Ji Jieping Ye Jianhui Chen Betul Ceran Qi Li Mingrui Wu			Learning Subspace Kernels for Classification				10.1145/1401890.1401908