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In particular, this is the first time that the task of finding\/fitting multiple parametric models to a dataset is formally posed as a consensus problem. We highlight the equivalence of these tasks and establish the connection with the computational Gestalt program, which seeks to provide a psychologically inspired detection theory for visual events. We also present a simple but powerful biclustering algorithm, specially tuned to the nature of the problem we address, though general enough to handle many different instances inscribed within our characterization. The presentation is accompanied with diverse and extensive experimental results in clustering, community detection, and multiple parametric model estimation in image processing applications.<\/jats:p>","DOI":"10.1137\/140967325","type":"journal-article","created":{"date-parts":[[2014,11,25]],"date-time":"2014-11-25T12:36:43Z","timestamp":1416919003000},"page":"2488-2525","source":"Crossref","is-referenced-by-count":11,"title":["A Biclustering Framework for Consensus Problems"],"prefix":"10.1137","volume":"7","author":[{"given":"Mariano","family":"Tepper","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillermo","family":"Sapiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2014,11,25]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0011585"},{"key":"atypb2","doi-asserted-by":"crossref","unstructured":"D. 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