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10.1007/s10044-020-00947-9 http://scihub22266oqcxt.onion/10.1007/s10044-020-00947-9 33424433!7781196!33424433     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Anal+Appl 2021 ; 24 (3): 887-905 Nephropedia Template TP {{tp|p=33424433|t=2021. A rotation based regularization method for semi-supervised learning |pdf=|usr=}}{{33424433}} gab.com Text A rotation based regularization method for semi-supervised learning JO: Pattern Anal Appl http://www.kidney.de/pget.php?&tw=1&pmid=33424433 #FOAvip In manifold learning, the intrinsic geometry of the manifold is explored and preserved by identifying the optimal local neighborhood around each observation. It is well known that when a Riemannian manifold is unfolded correctly, the observations lying spatially near to the manifold, should remain near on the lower dimension as well. Due to the nonlinear properties of manifold around each observation, finding such optimal neighborhood on the manifold is a challenge. Thus, a sub-optimal neighborhood may lead to erroneous representation and incorrect inferences. In this paper, we propose a rotation-based affinity metric for accurate graph Laplacian approximation. It exploits the property of aligned tangent spaces of observations in an optimal neighborhood to approximate correct affinity between them. Extensive experiments on both synthetic and real world datasets have been performed. It is observed that proposed method outperforms existing nonlinear dimensionality reduction techniques in low-dimensional representation for synthetic datasets. The results on real world datasets like COVID-19 prove that our approach increases the accuracy of classification by enhancing Laplacian regularization. Twit Text FOAVip A rotation based regularization method for semi-supervised learning ????Pattern Anal Appl http://www.kidney.de/pget.php?&tw=1&pmid=33424433 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33424433 #FOAvip English Wikipedia <ref>{{Cite pmid|33424433}}</ref> A rotation based regularization method for semi-supervised learning #MMPMID33424433 Shukla P; Abhishek; Verma S; Kumar M Pattern Anal Appl 2021[]; 24 (3): 887-905 PMID33424433show ga In manifold learning, the intrinsic geometry of the manifold is explored and preserved by identifying the optimal local neighborhood around each observation. It is well known that when a Riemannian manifold is unfolded correctly, the observations lying spatially near to the manifold, should remain near on the lower dimension as well. Due to the nonlinear properties of manifold around each observation, finding such optimal neighborhood on the manifold is a challenge. Thus, a sub-optimal neighborhood may lead to erroneous representation and incorrect inferences. In this paper, we propose a rotation-based affinity metric for accurate graph Laplacian approximation. It exploits the property of aligned tangent spaces of observations in an optimal neighborhood to approximate correct affinity between them. Extensive experiments on both synthetic and real world datasets have been performed. It is observed that proposed method outperforms existing nonlinear dimensionality reduction techniques in low-dimensional representation for synthetic datasets. The results on real world datasets like COVID-19 prove that our approach increases the accuracy of classification by enhancing Laplacian regularization. äA rotation based regularization method for semi-supervised learning (epmc).pdf DeepDyve Pubget Overpricing