A Kernel for Semi-Supervised Learning With Multi-View Point Cloud Regularization
نویسندگان
چکیده
In semi-supervised learning (SSL), we learn a predictive model from a collection of labeled data and a typically much larger collection of unlabeled data. These lecture notes present a framework called multi-view point cloud regularization (MVPCR) [5], which unifies and generalizes several semi-supervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHS). Special cases of MVPCR include co-regularized least squares (CoRLS) [7, 3, 6], manifold regularization (MR) [1, 8, 4], and graph-based semisupervised learning. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.
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In semi-supervised learning (SSL), we learn a predictive model from a collection of labeled data and a typically much larger collection of unlabeled data. These lecture notes present a framework called multi-view point cloud regularization (MVPCR) [5], which unifies and generalizes several semi-supervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbe...
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