Multiview point cloud kernels for semisupervised learning [Lecture Notes] Export

@article{citeulike:6043614, abstract = {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.}, author = {Rosenberg, D. and Sindhwani, V. and Bartlett, P. and Niyogi, P.}, citeulike-article-id = {6043614}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/MSP.2009.933383}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5230856}, day = {04}, doi = {10.1109/MSP.2009.933383}, journal = {Signal Processing Magazine, IEEE}, keywords = {graph-regularization, multiview, semi-supervised}, month = {September}, number = {5}, pages = {145--150}, posted-at = {2009-10-30 16:33:39}, priority = {2}, title = {Multiview point cloud kernels for semisupervised learning [Lecture Notes]}, url = {http://dx.doi.org/10.1109/MSP.2009.933383}, volume = {26}, year = {2009} }

TY - JOUR ID - citeulike:6043614 L3 - citeulike-article-id:6043614 N2 - 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. IS - 5 JF - Signal Processing Magazine, IEEE EP - 150 TI - Multiview point cloud kernels for semisupervised learning [Lecture Notes] VL - 26 SP - 145 KW - graph-regularization KW - multiview KW - semi-supervised AU - Rosenberg, D AU - Sindhwani, V AU - Bartlett, P AU - Niyogi, P PY - 2009/09/04/ UR - http://dx.doi.org/10.1109/MSP.2009.933383 ER -