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    Laplacian embedded regression for scalable manifold regularization by Chen, Lin, Tsang, Ivor Wai-Hung, Xu, Dong

    Published 2013
    “…Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. …”
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    Journal Article
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