{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T06:45:06Z","timestamp":1751093106200},"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":[[2020,7]]},"abstract":"<jats:p>We consider the problem of estimating a sparse Gaussian Graphical Model with a special graph topological structure and more than a million variables. Most previous scalable estimators still contain expensive calculation steps (e.g., matrix inversion or Hessian matrix calculation) and become infeasible in high-dimensional scenarios, where p (number of variables) is larger than n (number of samples). To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST). FST first obtains a graph structure by applying a generalized threshold to the sample covariance matrix. Then, it solves multiple block-wise subproblems via element-wise thresholding. By using matrix thresholding instead of matrix inversion as the computational bottleneck, FST reduces its computational complexity to a much lower order of magnitude (O(p2)). We show that FST obtains the same sharp convergence rate O(\u221a(log max{p, n}\/n) as other state-of-the-art methods. We validate the method empirically, on multiple simulated datasets and one real-world dataset, and show that FST is two times faster than the four baselines while achieving a lower error rate under both Frobenius-norm and max-norm.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/410","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"2964-2972","source":"Crossref","is-referenced-by-count":5,"title":["Quadratic Sparse Gaussian Graphical Model Estimation Method for Massive Variables"],"prefix":"10.24963","author":[{"given":"Jiaqi","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Software Engineering, Southeast University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Southeast University, China"},{"name":"School of Computer Science and Engineering, Southeast University, China"},{"name":"School of Artificial Intelligence, Southeast University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinchi","family":"Li","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Automation, YOUPEI College, Yancheng Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering, The University of Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Data Science & AI, Monash University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beilun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Southeast University, China"},{"name":"School of Computer Science and Engineering, Southeast University, China"},{"name":"School of Artificial Intelligence, Southeast University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-PRICAI-2020","name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","start":{"date-parts":[[2020,7,11]]},"theme":"Artificial Intelligence","location":"Yokohama, Japan","end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:14:59Z","timestamp":1594260899000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/410"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/410","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}