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Adaptive graph learning methods for clustering, which adjust a data similarity matrix while taking into account its clustering capability, have drawn increasing attention in recent years due to their promising clustering performance. Existing adaptive graph learning methods are based on either original data or linearly projected data and thus rely on the assumption that either representation is a...
A center sliding Bayesian design adopting orthogonal polynomials for binary pattern classification is studied in this paper. Essentially, a Bayesian weight solution is coupled with a center sliding scheme in feature space which provides an easy tuning capability for binary classification. The proposed method is compared with several state-of-the-art binary classifiers in terms of their solution forms,...
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