{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:31:51Z","timestamp":1723015911859},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>The graphical lasso is the most popular approach to estimating the inverse covariance matrix of high-dimension data. It iteratively estimates each row and column of the matrix in a round-robin style until convergence. However, the graphical lasso is infeasible due to its high computation cost for large size of datasets. This paper proposes Sting, a fast approach to the graphical lasso. In order to reduce the computation cost, it efficiently identifies blocks in the estimated matrix that have nonzero elements before entering the iterations by exploiting the singular value decomposition of data matrix. In addition, it selectively updates elements of the estimated matrix expected to have nonzero values. Theoretically, it guarantees to converge to the same result as the original algorithm of the graphical lasso. Experiments show that our approach is faster than existing approaches.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/233","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"1682-1688","source":"Crossref","is-referenced-by-count":0,"title":["SVD-Based Screening for the Graphical Lasso"],"prefix":"10.24963","author":[{"given":"Yasuhiro","family":"Fujiwara","sequence":"first","affiliation":[{"name":"NTT Software Innovation Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoki","family":"Marumo","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathieu","family":"Blondel","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koh","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hideaki","family":"Kim","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomoharu","family":"Iwata","sequence":"additional","affiliation":[{"name":"NTT Corporation, NTT Communication Science Laboratories, NTT"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naonori","family":"Ueda","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:52:55Z","timestamp":1501228375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/233"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/233","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}