{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:34:10Z","timestamp":1723016050272},"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":[[2019,8]]},"abstract":"<jats:p>Semi-supervised learning (SSL) plays an increasingly important role in the big data era because a large number of unlabeled samples can be used effectively to improve the performance of the classifier. Semi-supervised support vector machine (S3VM) is one of the most appealing methods for SSL, but scaling up S3VM for kernel learning is still an open problem. Recently, a doubly stochastic gradient (DSG) algorithm has been proposed to achieve efficient and scalable training for kernel methods. However, the algorithm and theoretical analysis of DSG are developed based on the convexity assumption which makes them incompetent for non-convex problems such as S3VM. To address this problem, in this paper, we propose a triply stochastic gradient algorithm for S3VM, called TSGS3VM. Specifically, to handle two types of data instances involved in S3VM, TSGS3VM samples a labeled instance and  an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient. We use the approximated gradient to update the  solution. More importantly, we establish new theoretic analysis for TSGS3VM which guarantees that TSGS3VM can converge to a stationary point. Extensive experimental results on a variety of datasets demonstrate that TSGS3VM is much more efficient and scalable than existing S3VM algorithms.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/328","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"2364-2370","source":"Crossref","is-referenced-by-count":6,"title":["Scalable Semi-Supervised SVM via Triply Stochastic Gradients"],"prefix":"10.24963","author":[{"given":"Xiang","family":"Geng","sequence":"first","affiliation":[{"name":"School of Computer & Software, Nanjing University of Information Science & Technology, P.R.China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Computer & Software, Nanjing University of Information Science & Technology, P.R.China"},{"name":"JD Finance America Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Western Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanli","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer & Software, Nanjing University of Information Science & Technology, P.R.China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guansheng","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer & Software, Nanjing University of Information Science & Technology, P.R.China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, USA"},{"name":"JD Finance America Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:48:35Z","timestamp":1564300115000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/328"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/328","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}