{"id":518,"date":"2017-06-01T00:00:00","date_gmt":"2017-06-01T00:00:00","guid":{"rendered":"http:\/\/web.eecs.umich.edu\/~girasole\/?p=518"},"modified":"2018-03-12T15:04:25","modified_gmt":"2018-03-12T15:04:25","slug":"distance-penalized-active-learning-using-quantile-search","status":"publish","type":"post","link":"https:\/\/web.eecs.umich.edu\/~girasole\/?p=518","title":{"rendered":"Distance-Penalized Active Learning Using Quantile Search"},"content":{"rendered":"<p>Active sampling &#8212; where one chooses what samples to collect based on data collected thus far &#8212; is an important approach for spatial environmental sampling, where resources are drastically limited when compared to the extent of the signals of interest. However, most active learning literature studies the case where each sample has equal cost. In spatial sampling, the sample cost is often proportional to distance between samples. John Lipor and I collaborated with our colleagues in the department of Civil and Environmental Engineering and the department of Natural Resources to develop active sampling techniques for lake sampling.<\/p>\n<p>The code is <a href=\"http:\/\/web.cecs.pdx.edu\/~lipor\/Code\/lipor2017distance.zip\">available here<\/a>. You can also find a video about the <a href=\"http:\/\/web.eecs.umich.edu\/~girasole\/?p=397\">project here<\/a>.<\/p>\n<p>Lipor, J., B. P. Wong, D. Scavia, B. Kerkez, and L. Balzano. 2017. \u201cDistance-Penalized Active Learning Using Quantile Search.\u201d <i>IEEE Transactions on Signal Processing<\/i> 65 (20): 5453\u201365. <a href=\"https:\/\/doi.org\/10.1109\/TSP.2017.2731323\">https:\/\/doi.org\/10.1109\/TSP.2017.2731323<\/a>.<\/p>\n<p>Lipor, J., L. Balzano, B. Kerkez, and D. Scavia. 2015. \u201cQuantile Search: A Distance-Penalized Active Learning Algorithm for Spatial Sampling.\u201d In <i>2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)<\/i>, 1241\u201348. <a href=\"https:\/\/doi.org\/10.1109\/ALLERTON.2015.7447150\">https:\/\/doi.org\/10.1109\/ALLERTON.2015.7447150<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Active sampling &#8212; where one chooses what samples to collect based on data collected thus far &#8212; is an important approach for spatial environmental sampling, where resources are drastically limited when compared to the extent of the signals of interest. However, most active learning literature studies the case where each sample has equal cost. In [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/posts\/518"}],"collection":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=518"}],"version-history":[{"count":2,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/posts\/518\/revisions"}],"predecessor-version":[{"id":520,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/posts\/518\/revisions\/520"}],"wp:attachment":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=518"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=518"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=518"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}