{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T11:54:39Z","timestamp":1673092479451},"reference-count":36,"publisher":"MIT Press - Journals","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2017,7]]},"abstract":"<jats:p> Since combining features from heterogeneous data sources can significantly boost classification performance in many applications, it has attracted much research attention over the past few years. Most of the existing multiview feature analysis approaches separately learn features in each view, ignoring knowledge shared by multiple views. Different views of features may have some intrinsic correlations that might be beneficial to feature learning. Therefore, it is assumed that multiviews share subspaces from which common knowledge can be discovered. In this letter, we propose a new multiview feature learning algorithm, aiming to exploit common features shared by different views. To achieve this goal, we propose a feature learning algorithm in a batch mode, by which the correlations among different views are taken into account. Multiple transformation matrices for different views are simultaneously learned in a joint framework. In this way, our algorithm can exploit potential correlations among views as supplementary information that further improves the performance result. Since the proposed objective function is nonsmooth and difficult to solve directly, we propose an iterative algorithm for effective optimization. Extensive experiments have been conducted on a number of real-world data sets. Experimental results demonstrate superior performance in terms of classification against all the compared approaches. Also, the convergence guarantee has been validated in the experiment. <\/jats:p>","DOI":"10.1162\/neco_a_00977","type":"journal-article","created":{"date-parts":[[2017,5,31]],"date-time":"2017-05-31T18:01:02Z","timestamp":1496253662000},"page":"1986-2003","source":"Crossref","is-referenced-by-count":5,"title":["Multiview Feature Analysis via Structured Sparsity and Shared Subspace Discovery"],"prefix":"10.1162","volume":"29","author":[{"given":"Yan-Shuo","family":"Chang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Software Park, and Institute for Silk Road Research, Xi'an 71027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[{"name":"OPTIMA, Northwestern Polytechnical University, Xi'an 71027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University and Software Park, Xi'an 71027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"B1","author":"Bay H.","year":"2006","journal-title":"Proceedings of the 9th European Conference on Computer Vision"},{"key":"B2","author":"Cai X.","year":"2013","journal-title":"Proceedings of the Conference on Artificial Intelligence"},{"issue":"3","key":"B3","first-page":"27:1","volume":"2","author":"Chang C.-C.","year":"2011","journal-title":"ACM TIST"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2441735"},{"key":"B5","first-page":"1171","author":"Chang X.","year":"2014","journal-title":"Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1145\/2910585"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.208"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2608901"},{"key":"B9","author":"Chen H.","year":"2012","journal-title":"Proceedings of the Conference on Medical Computing and Computer-Assisted Intervention"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1145\/1646396.1646452"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38886-6_43"},{"key":"B12","author":"Dalal N.","year":"2005","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"B13","author":"Fan M.","year":"2017","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"B14","author":"Foster D. 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