Non-Parametric Kernel Learning with robust pairwise constraints
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Chen, Changyou; Zhang, Junping; He, Xuefang; Zhou, Zhi-Hua
Description
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 |
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Date published: | 2012 |
Type: | Journal article |
URI: | http://hdl.handle.net/1885/71747 |
Source: | International Journal of Machine Learning and Cybernetics |
DOI: | 10.1007/s13042-011-0048-6 |
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01_Chen_Non-Parametric_Kernel_Learning_2012.pdf | 1.07 MB | Adobe PDF | Request a copy |
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