Learning subspace kernels for classification

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 le...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Jianhui Chen, Shuiwang Ji, Betul Ceran, Qi Li, Mingrui Wu, Jieping Ye
• Keywords: Data Mining, KDD 2008, Optimal Subspace Kernel, Subspace Kernel, Uncorrelated Subspace Kernel