{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:06:38Z","timestamp":1765357598922},"reference-count":51,"publisher":"MIT Press","issue":"7","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA, where the sparsity could be considered as a Laplace prior on the canonical variates, works only for two data sets, that is, there are only two views or two distinct objects. To overcome this limitation, we propose a sparse generalized canonical correlation analysis (GCCA), which could detect the latent relations of multiview data with sparse structures. Specifically, we convert the GCCA into a linear system of equations\u00a0and impose \u21131 minimization penalty to pursue sparsity. This results in a nonconvex problem on the Stiefel manifold. Based on consensus optimization, a distributed alternating iteration approach is developed, and consistency is investigated elaborately under mild conditions. Experiments on several synthetic and real-world data sets demonstrate the effectiveness of the proposed algorithm.<\/jats:p>","DOI":"10.1162\/neco_a_01673","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T22:53:00Z","timestamp":1716418380000},"page":"1380-1409","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration-Based Approach"],"prefix":"10.1162","volume":"36","author":[{"given":"Kexin","family":"Lv","sequence":"first","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China Kelen_Lv@sjtu.edu.cn"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Digital Economics, Guangdong University of Finance and Economics, 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