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Thus, all tuning parameters such as the kernel width or the regularization parameter can be objectively optimized. This is an advantage over recently developed kernel-based independence measures and is a highly useful property in unsupervised learning problems such as ICA. Based on this novel independence measure, we develop an ICA algorithm, named least-squares independent component analysis.<\/jats:p>","DOI":"10.1162\/neco_a_00062","type":"journal-article","created":{"date-parts":[[2010,10,22]],"date-time":"2010-10-22T01:52:21Z","timestamp":1287712341000},"page":"284-301","source":"Crossref","is-referenced-by-count":24,"title":["Least-Squares Independent Component Analysis"],"prefix":"10.1162","volume":"23","author":[{"given":"Taiji","family":"Suzuki","sequence":"first","affiliation":[{"name":"Department of Mathematical Informatics, University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan"}]},{"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tokyo Institute of Technology and PRESTO, Japan Science and Technology Agency, Meguro-ku, Tokyo 152-8552, Japan"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1111\/j.2517-6161.1966.tb00626.x","volume":"28","author":"Ali S. 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