Non-Parametric Kernel Learning with robust pairwise constraints
For existing kernel learning based semi-supervised clustering algorithms, it is generally difficult to scale well with large scale datasets and robust pairwise constraints. In this paper, we propose a new Non-Parametric Kernel Learning (NPKL) framework to deal with these problems. We generalize the graph embedding framework into kernel learning, by reforming it as a semi-definitive programming (SDP) problem, smoothing and avoiding over-smoothing the functional Hilbert space with Laplacian...[Show more]
|Collections||ANU Research Publications|
|Source:||International Journal of Machine Learning and Cybernetics|
|01_Chen_Non-Parametric_Kernel_Learning_2012.pdf||1.07 MB||Adobe PDF||Request a copy|
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