Unsupervised non-parametric kernel learning algorithm

A foundational problem in kernel-based learning is how to design suitable kernels. Non-Parametric Kernel Learning (NPKL) is one of the most important kernel learning methods. However, most research on NPKL has tended to focus on the semi-supervised scenario. In this paper, we propose a novel unsuper...

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Veröffentlicht in:	Knowledge-based systems Jg. 44; S. 1 - 9
Hauptverfasser:	Liu, Bing, Xia, Shi-Xiong, Zhou, Yong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.05.2013
Schlagworte:	Algorithms Clustering Data Exact solutions Kernels Knowledge base Learning Least squares method Machine learning Manifold regularized least-squares Mathematical analysis Maximum margin clustering (MMC) Multiple kernel learning Non-parametric kernel learning Sparse eigen-decomposition Spectra Multiple kernel learning Manifold regularized least-squares Maximum margin clustering (MMC) Non-parametric kernel learning Sparse eigen-decomposition
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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