{"id":1186,"date":"2020-10-23T16:09:04","date_gmt":"2020-10-23T15:09:04","guid":{"rendered":"https:\/\/babel.isa.uma.es\/kipr\/?p=1186"},"modified":"2020-10-23T16:09:04","modified_gmt":"2020-10-23T15:09:04","slug":"nice-related-work-on-change-point-detection-and-a-novel-algorithm-for-off-line-detection-of-abrupt-changes-in-multivariate-signals","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=1186","title":{"rendered":"Nice related work on change-point detection and a novel algorithm for off-line detection of abrupt changes in multivariate signals"},"content":{"rendered":"<h4>Charles Truong; Laurent Oudre; Nicolas Vayatis, <strong>Greedy Kernel Change-Point Detection,<\/strong> IEEE Transactions on Signal Processing ( Volume: 67, Issue: 24, Dec.15, 15 2019), <a href=\"https:\/\/doi.org\/10.1109\/TSP.2019.2953670\" target=\"_blank\">DOI: 10.1109\/TSP.2019.2953670<\/a>.<\/h4>\n<blockquote><p>We consider the problem of detecting abrupt changes in the underlying stochastic structure of multivariate signals. A novel non-parametric and model-free off-line change-point detection method based on a kernel mapping is presented. This approach is sequential and alternates between two steps: a greedy detection to estimate a new breakpoint and a projection to remove its contribution to the signal. The resulting algorithm is able to segment time series for which no accurate model is available: it is computationally more efficient than exact kernel change-point detection and more precise than window-based approximations. The proposed method also offers some theoretical consistency properties. For the special case of a linear kernel, an even faster implementation is provided. The proposed strategy is compared to standard parametric and non-parametric procedures on a real-world data set composed of 262 accelerometer recordings.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>Charles Truong; Laurent Oudre; Nicolas Vayatis, Greedy Kernel Change-Point Detection, IEEE Transactions on Signal Processing ( Volume: 67, Issue: 24, <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=1186\" class=\"more-link\"><span>Read More &rarr;<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41],"tags":[30,249,47],"class_list":["post-1186","post","type-post","status-publish","format-standard","hentry","category-probability-and-statistics","tag-change-detection","tag-changepoint-detection","tag-time-series-analysis"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1186"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1186"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1186\/revisions"}],"predecessor-version":[{"id":1187,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1186\/revisions\/1187"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1186"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1186"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1186"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}