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10.1007/978-3-319-23525-7_46 http://scihub22266oqcxt.onion/10.1007/978-3-319-23525-7_46 C4688020!4688020!26705541     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Mach+Learn+Knowl+Discov+Databases 2015 ; 9285 (ä): 752-67 Nephropedia Template TP {{tp|p=26705541|t=2015. Robust Classification of Information Networks by Consistent Graph Learning |pdf=|usr=}}{{26705541}} gab.com Text Robust Classification of Information Networks by Consistent Graph Learning JO: Mach Learn Knowl Discov Databases http://www.kidney.de/pget.php?&tw=1&pmid=26705541 #FOAvip Graph regularization-based methods have achieved great success for network classification by making the label-link consistency assumption, i.e., if two nodes are linked together, they are likely to belong to the same class. However, in a real-world network, there exist links that connect nodes of different classes. These inconsistent links raise a big challenge for graph regularization and deteriorate the classification performance significantly. To address this problem, we propose a novel algorithm, namely Consistent Graph Learning, which is robust to the inconsistent links of a network. In particular, given a network and a small number of labeled nodes, we aim at learning a consistent network with more consistent and fewer inconsistent links than the original network. Since the link information of a network is naturally represented by a set of relation matrices, the learning of a consistent network is reduced to learning consistent relation matrices under some constraints. More specifically, we achieve it by joint graph regularization on the nuclear norm minimization of consistent relation matrices together with ?1-norm minimization on the difference matrices between the original relation matrices and the learned consistent ones subject to certain constraints. Experiments on both homogeneous and heterogeneous network datasets show that the proposed method outperforms the state-of-the-art methods. Twit Text FOAVip Robust Classification of Information Networks by Consistent Graph Learning ????Mach Learn Knowl Discov Databases http://www.kidney.de/pget.php?&tw=1&pmid=26705541 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26705541 #FOAvip English Wikipedia <ref>{{Cite pmid|26705541}}</ref> Robust Classification of Information Networks by Consistent Graph Learning #MMPMID26705541 Zhi S; Han J; Gu Q Mach Learn Knowl Discov Databases 2015[Sep]; 9285 (ä): 752-67 PMID26705541show ga Graph regularization-based methods have achieved great success for network classification by making the label-link consistency assumption, i.e., if two nodes are linked together, they are likely to belong to the same class. However, in a real-world network, there exist links that connect nodes of different classes. These inconsistent links raise a big challenge for graph regularization and deteriorate the classification performance significantly. To address this problem, we propose a novel algorithm, namely Consistent Graph Learning, which is robust to the inconsistent links of a network. In particular, given a network and a small number of labeled nodes, we aim at learning a consistent network with more consistent and fewer inconsistent links than the original network. Since the link information of a network is naturally represented by a set of relation matrices, the learning of a consistent network is reduced to learning consistent relation matrices under some constraints. More specifically, we achieve it by joint graph regularization on the nuclear norm minimization of consistent relation matrices together with ?1-norm minimization on the difference matrices between the original relation matrices and the learned consistent ones subject to certain constraints. Experiments on both homogeneous and heterogeneous network datasets show that the proposed method outperforms the state-of-the-art methods. äRobust Classification of Information Networks by Consistent Graph Lear(epmc).pdf DeepDyve Pubget Overpricing