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On Efficient Feature Ranking Methods for High-throughput Data Analysis

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SKU: PROJ5056 Categories: 2016 Projects, Bio Medical, 2015 Projects, Final Year Projects, Online Java Projects Tags: academic projects, Android project 2013-2014, Android project 2015-2016, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project 2015-2016, dotnet project Abstract, dotnet project list, elysium technologies abstract, elysium technologies chennai, elysium technologies coimbatore, elysium technologies company, elysium technologies courses, elysium technologies erode, elysium technologies inpant traning, elysium technologies internship, elysium technologies jobs, elysium technologies madurai, elysium technologies mou, elysium technologies pondychery, elysium technologies projectlist, elysium technologies projects, elysium technologies ramnad, elysium technologies salem, elysium technologies software, elysium technologies tirunelveli, elysium technologies trichy, Final Year Projects, java projects 2013-2014, java projects 2015-2016, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project 2015-2016, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project 2015-2016, Php project Abstract, Php project list, Power Electronic project 2013-2014, Power Electronic project 2015-2016, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, Vlsi project 2013-2014, Vlsi project 2015-2016, Vlsi project Abstract, Vlsi project list
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On Efficient Feature Ranking Methods for High-throughput Data Analysis

Abstract— On Efficient Feature Ranking Methods for High-throughput Data Analysis. Efficient mining of high-throughput data has become one of the popular themes in the big data era. Existing biology related feature ranking methods mainly focus on statistical and annotation information. In this study, two efficient feature ranking methods are presented. Multi-target regression and graph embedding are < Final Year Projects 2016 > in an optimization framework, and feature ranking is achieved by introducing structured sparsity norm. Unlike existing methods, the presented methods have two advantages: (1) the feature subset simultaneously account for global margin information as well as locality manifold information. Consequently, both global and locality information are considered. (2) Features are selected by batch rather than individually in the algorithm framework.

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