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In our method, the \u21131-regularization term is added to the cost function of ICA, and minimization of the cost function is performed by a difference of convex functions algorithm. For the validity of our proposed method, we apply it to synthetic data and real functional magnetic resonance imaging data.<\/jats:p>","DOI":"10.1162\/neco_a_01709","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T17:07:43Z","timestamp":1727111263000},"page":"2540-2570","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["\u2113\n                1\n          -Regularized ICA: A Novel Method for Analysis of Task-Related fMRI Data"],"prefix":"10.1162","volume":"36","author":[{"given":"Yusuke","family":"Endo","sequence":"first","affiliation":[{"name":"Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Ibaraki 316-8511, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koujin","family":"Takeda","sequence":"additional","affiliation":[{"name":"Department of Mechanical Systems Engineering, 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