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Semi-supervised kernel learning is attracting increasing research interests recently. It works by learning an embedding of data from the input space to a Hilbert space using both labeled data and unlabeled data, and then searching for relations among the embedded data points. One of the most well-known semi-supervised kernel learning approaches is the spectral kernel learning methodology which usually tunes the spectral empirically or through optimizing some generalized performance measures.doi:10.1109/ijcnn.2007.4370993 dblp:conf/ijcnn/XuZLK07 fatcat:mnoo2eij7nflvpyjmdyhkl3kay