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10.1007/s10489-020-01986-9 http://scihub22266oqcxt.onion/10.1007/s10489-020-01986-9 C7677445Cross-covariancebasedaffinityforgraphs.!7677445 !34764570     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\34764570 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Appl+Intell+(Dordr) 2021 ; 51 (6 ): 3844-3864 Nephropedia Template TP {{tp|p=34764570 |t=2021. Cross-covariance based affinity for graphs |pdf=|usr=}}{{34764570 }} gab.com Text Cross-covariance based affinity for graphs JO: Appl Intell (Dordr) http://www.kidney.de/pget.php?&tw=1&pmid=34764570 #FOAvip The accuracy of graph based learning techniques relies on the underlying topological structure and affinity between data points, which are assumed to lie on a smooth Riemannian manifold. However, the assumption of local linearity in a neighborhood does not always hold true. Hence, the Euclidean distance based affinity that determines the graph edges may fail to represent the true connectivity strength between data points. Moreover, the affinity between data points is influenced by the distribution of the data around them and must be considered in the affinity measure. In this paper, we propose two techniques, C C G A (L) and C C G A (N) that use cross-covariance based graph affinity (CCGA) to represent the relation between data points in a local region. C C G A (L) also explores the additional connectivity between data points which share a common local neighborhood. C C G A (N) considers the influence of respective neighborhoods of the two immediately connected data points, which further enhance the affinity measure. Experimental results of manifold learning on synthetic datasets show that CCGA is able to represent the affinity measure between data points more accurately. This results in better low dimensional representation. Manifold regularization experiments on standard image dataset further indicate that the proposed CCGA based affinity is able to accurately identify and include the influence of the data points and its common neighborhood that increase the classification accuracy. The proposed method outperforms the existing state-of-the-art manifold regularization methods by a significant margin. Twit Text FOAVip Cross-covariance based affinity for graphs ????Appl Intell (Dordr) http://www.kidney.de/pget.php?&tw=1&pmid=34764570 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34764570 #FOAvip English Wikipedia <ref>{{Cite pmid|34764570 }}</ref> Cross-covariance based affinity for graphs #MMPMID34764570 Yadav RK ; Abhishek ; Verma S ; Venkatesan S Appl Intell (Dordr) 2021[]; 51 (6 ): 3844-3864 PMID34764570 show ga The accuracy of graph based learning techniques relies on the underlying topological structure and affinity between data points, which are assumed to lie on a smooth Riemannian manifold. However, the assumption of local linearity in a neighborhood does not always hold true. Hence, the Euclidean distance based affinity that determines the graph edges may fail to represent the true connectivity strength between data points. Moreover, the affinity between data points is influenced by the distribution of the data around them and must be considered in the affinity measure. In this paper, we propose two techniques, C C G A (L) and C C G A (N) that use cross-covariance based graph affinity (CCGA) to represent the relation between data points in a local region. C C G A (L) also explores the additional connectivity between data points which share a common local neighborhood. C C G A (N) considers the influence of respective neighborhoods of the two immediately connected data points, which further enhance the affinity measure. Experimental results of manifold learning on synthetic datasets show that CCGA is able to represent the affinity measure between data points more accurately. This results in better low dimensional representation. Manifold regularization experiments on standard image dataset further indicate that the proposed CCGA based affinity is able to accurately identify and include the influence of the data points and its common neighborhood that increase the classification accuracy. The proposed method outperforms the existing state-of-the-art manifold regularization methods by a significant margin. äCross-covariance based affinity for graphs (epmc).pdf DeepDyve Pubget Overpricing