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dc.contributor.advisorPolina Golland.en_US
dc.contributor.authorChen, George Hen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2012-12-13T19:19:02Z
dc.date.available2012-12-13T19:19:02Z
dc.date.copyright2012en_US
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/75709
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 87-91).en_US
dc.description.abstractSparse coding represents input signals each as a sparse linear combination of a set of basis or dictionary elements where sparsity encourages representing each input signal with a few of the most indicative dictionary elements. In this thesis, we extend sparse coding to allow dictionary elements to undergo deformations, resulting in a general probabilistic model and accompanying inference algorithm for estimating sparse linear combination weights, dictionary elements, and deformations. We apply our proposed method on functional magnetic resonance imaging (fMRI) data, where the locations of functional regions in the brain evoked by a specific cognitive task may vary across individuals relative to anatomy. For a language fMRI study, our method identifies activation regions that agree with known literature on language processing. Furthermore, the deformations learned by our inference algorithm produce more robust group-level effects than anatomical alignment alone.en_US
dc.description.statementofresponsibilityby George H. Chen.en_US
dc.format.extent91 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleDeformation-invariant sparse codingen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc818354481en_US


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