Multiview point cloud kernels for semisupervised learning Lecture Notes

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 kerne...

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Veröffentlicht in:	IEEE signal processing magazine 2009-01, Vol.26 (5)
Hauptverfasser:	Rosenberg, D, Sindhwani, V, Bartlett, P, Niyogi, P
Format:	Magazinearticle
Sprache:	eng
Schlagworte:	Clouds Collection Kernels Learning Least squares method Lectures Regularization
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container_title	IEEE signal processing magazine
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creator	Rosenberg, D Sindhwani, V Bartlett, P Niyogi, P
description	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.
doi_str_mv	10.1109/MSP.2009.933383
format	Magazinearticle
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subjects	Clouds Collection Kernels Learning Least squares method Lectures Regularization
title	Multiview point cloud kernels for semisupervised learning Lecture Notes
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