{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:17:22Z","timestamp":1760239042012,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,12]],"date-time":"2020-09-12T00:00:00Z","timestamp":1599868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11601081"],"award-info":[{"award-number":["11601081"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the research fund for distinguished young scholars of Fujian Agriculture and Forestry University","award":["xjq201727"],"award-info":[{"award-number":["xjq201727"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In the large scale canonical correlation analysis arising from multi-view learning applications, one needs to compute canonical weight vectors corresponding to a few of largest canonical correlations. For such a task, we propose a Jacobi\u2013Davidson type algorithm to calculate canonical weight vectors by transforming it into the so-called canonical correlation generalized eigenvalue problem. Convergence results are established and reveal the accuracy of the approximate canonical weight vectors. Numerical examples are presented to support the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/a13090229","type":"journal-article","created":{"date-parts":[[2020,9,13]],"date-time":"2020-09-13T21:11:32Z","timestamp":1600031492000},"page":"229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Jacobi\u2013Davidson Method for Large Scale Canonical Correlation Analysis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1533-1534","authenticated-orcid":false,"given":"Zhongming","family":"Teng","sequence":"first","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1162\/0899766042321814","article-title":"Canonical correlation analysis: An overview with application to learning methods","volume":"16","author":"Hardoon","year":"2004","journal-title":"Neural Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1093\/biomet\/28.3-4.321","article-title":"Relations between two sets of variates","volume":"28","author":"Harold","year":"1936","journal-title":"Biometrika"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1080\/10556788.2019.1700257","article-title":"Orthogonal canonical correlation analysis and applications","volume":"35","author":"Wang","year":"2020","journal-title":"Opt. 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