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10.1073/pnas.1700770114 http://scihub22266oqcxt.onion/10.1073/pnas.1700770114 C5603997!5603997 !28851838     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28851838 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Proc+Natl+Acad+Sci+U+S+A 2017 ; 114 (37 ): 9814-9819 Nephropedia Template TP {{tp|p=28851838 |t=2017. Robust continuous clustering |pdf=|usr=}}{{28851838 }} gab.com Text Robust continuous clustering JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=28851838 #FOAvip Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. Twit Text FOAVip Robust continuous clustering ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=28851838 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28851838 #FOAvip English Wikipedia <ref>{{Cite pmid|28851838 }}</ref> Robust continuous clustering #MMPMID28851838 Shah SA ; Koltun V Proc Natl Acad Sci U S A 2017[Sep]; 114 (37 ): 9814-9819 PMID28851838 show ga Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. äRobust continuous clustering (epmc).pdf DeepDyve Pubget Overpricing