2026-09-18T20:23:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/757092022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Polina Golland.
Chen, George H
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2012-12-13T19:19:02Z
2012-12-13T19:19:02Z
2012
2012
http://hdl.handle.net/1721.1/75709
818354481
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 87-91).
Sparse 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.
by George H. Chen.
S.M.
91 p.
eng
Massachusetts Institute of Technology
M.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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Deformation-invariant sparse coding
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ce3d98ea-b62b-4394-a810-017b05ff84ad">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Deformation-invariant sparse coding</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2012</PublicationDate>
<Authors>
<Author>
<DisplayName>Chen, George H</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>Sparse 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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>