{"id":65,"date":"2020-06-01T16:39:34","date_gmt":"2020-06-01T16:39:34","guid":{"rendered":"http:\/\/mlhpc2.peter-zaspel.de\/?p=65"},"modified":"2020-06-15T17:18:10","modified_gmt":"2020-06-15T17:18:10","slug":"fast-kernel-ridge-regression-by-matrix-approximation-techniques","status":"publish","type":"post","link":"https:\/\/www.peter-zaspel.de\/?p=65","title":{"rendered":"Fast Kernel Ridge Regression by matrix approximation techniques"},"content":{"rendered":"\n<p>The topic of this project is the efficient training of Machine Learning by Kernel Ridge Regression.<\/p>\n\n\n\n<p>Relevant content will be:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Kernel Ridge Regression<\/li><li>iterative solvers for linear systems<\/li><li>matrix approximation techniques:<ul><li>low rank approximation (SVD, ACA, &#8230;)<\/li><li>Askit<\/li><li>Hierarchical Matrices<\/li><\/ul><\/li><\/ul>\n\n\n\n<p>Application data should be large-scale and science-related. Maybe the first starting point would be data from quantum chemistry that I have access to.<br>The beauty of this project would be to further develop and analyze the impact of non-exact solvers for linear systems on the quality of the prediction of Kernel Ridge Regression. This is highly research relevant.<\/p>\n\n\n\n<p><strong>WARNING:<\/strong> Some flavor of this topic (e.g. hierarchical matrices) requires a profound mathematical background.<br><br>Some first links:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"http:\/\/www.cs.nyu.edu\/~mohri\/pub\/kst.pdf\">http:\/\/www.cs.nyu.edu\/~mohri\/pub\/kst.pdf<\/a><\/li><li><a href=\"http:\/\/dept.stat.lsa.umich.edu\/~jizhu\/pubs\/Mukherjee-SADM11.pdf\">http:\/\/dept.stat.lsa.umich.edu\/~jizhu\/pubs\/Mukherjee-SADM11.pdf<\/a><\/li><li><a href=\"https:\/\/arxiv.org\/abs\/1410.0260\">https:\/\/arxiv.org\/abs\/1410.0260<\/a><\/li><li><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0955799702001522\">https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0955799702001522<\/a><\/li><li><a href=\"http:\/\/jmlr.org\/papers\/volume18\/15-025\/15-025.pdf\">http:\/\/jmlr.org\/papers\/volume18\/15-025\/15-025.pdf<\/a><\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The topic of this project is the efficient training of Machine Learning by Kernel Ridge Regression. Relevant content will be: Kernel Ridge Regression iterative solvers for linear systems matrix approximation techniques: low rank approximation (SVD, ACA, &#8230;) Askit Hierarchical Matrices<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[4],"tags":[],"class_list":["post-65","post","type-post","status-publish","format-standard","hentry","category-summer-research-topics"],"_links":{"self":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/posts\/65","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=65"}],"version-history":[{"count":2,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/posts\/65\/revisions"}],"predecessor-version":[{"id":119,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/posts\/65\/revisions\/119"}],"wp:attachment":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=65"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=65"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=65"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}