Conference Proceeding Article
Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Some kernel learning methods assume the target kernel matrix to be a linear combination of parametric kernel matrices. This assumption again importantly limits the flexibility of the target kernel matrices. The key challenge with nonparametric kernel learning arises from the difficulty in linking the nonparametric kernels to the input patterns. In this paper, we resolve this problem by introducing the graph Laplacian of the observed data as a regularizer when optimizing the kernel matrix with respect to the pairwise constraints. We formulate the problem into Semi-Definite Programs (SDP), and propose an efficient algorithm to solve the SDP problem. The extensive evaluation on clustering with pairwise constraints shows that the proposed nonparametric kernel learning method is more effective than other state-of-the-art kernel learning techniques.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Proceedings of the 24th International Conference on Machine Learning: Corvalis, Oregon, June 20-24, 2007
City or Country
HOI, Steven; JIN, Rong; and LYU, Michael R..
Learning Nonparametric Kernel Matrices from Pairwise Constraints. (2007). Proceedings of the 24th International Conference on Machine Learning: Corvalis, Oregon, June 20-24, 2007. 361-368. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2384
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