Chen, Changyou; Zhang, Junping; He, Xuefang; Zhou, Zhi-Hua
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]
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