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Parameter-Free Spectral Kernel Learning
[article]
2012
arXiv
pre-print
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing attention in machine learning. In this paper, we propose a novel semi-supervised kernel learning method which can seamlessly combine manifold structure of unlabeled data and Regularized Least-Squares (RLS) to learn a new kernel. Interestingly, the new kernel matrix can be obtained analytically with the use of spectral decomposition of graph Laplacian matrix. Hence, the proposed algorithm does
arXiv:1203.3495v1
fatcat:5rohnaxa5za2vkdct4m4sveyka