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Multiview point cloud kernels for semisupervised learning [Lecture Notes]

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4 Author(s)
Rosenberg, D. ; Univ. of California, Berkeley, CA, USA ; Sindhwani, V. ; Bartlett, P.L. ; Niyogi, P.

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.

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Signal Processing Magazine, IEEE  (Volume:26 ,  Issue: 5 )