2026-09-18T19:23:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1089772022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Lizhong Zheng.
Qiu, David.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2017-05-11T19:58:49Z
2017-05-11T19:58:49Z
2017
2017
http://hdl.handle.net/1721.1/108977
986497419
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 45-46).
Finding low dimensional latent variable models is a useful technique in inferring unobserved affinity between unobserved co-occurrences. We explore using maximal correlation and the alternating conditional expectation algorithm to construct embeddings one dimensional at a time to maximally preserve the linear correlation in the embedding space. Each dimension is enforced to be orthogonal to all other dimensions to not encode redundant information. Intuitively, we want to map objects that frequently co-occur to be close in the embedding space. However, often there are unobserved or under-sampled pairs that skew the result. We derive simple regularization techniques to compensate for those outliers. Additionally, optimizing for the preservation of maximal correlations after processing lets us induce informative soft clustering and mixture models. Empirical results on natural language processing datasets show that our technique performs comparably to popular word embedding algorithms.
by David Qiu.
S.M.
46 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Embedding and latent variable models using maximal correlation
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ca63fe1b-a62d-4662-93e0-605e6f77cb1f">
<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>Embedding and latent variable models using maximal correlation</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2017</PublicationDate>
<Authors>
<Author>
<DisplayName>Qiu, David.</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>Finding low dimensional latent variable models is a useful technique in inferring unobserved affinity between unobserved co-occurrences. We explore using maximal correlation and the alternating conditional expectation algorithm to construct embeddings one dimensional at a time to maximally preserve the linear correlation in the embedding space. Each dimension is enforced to be orthogonal to all other dimensions to not encode redundant information. Intuitively, we want to map objects that frequently co-occur to be close in the embedding space. However, often there are unobserved or under-sampled pairs that skew the result. We derive simple regularization techniques to compensate for those outliers. Additionally, optimizing for the preservation of maximal correlations after processing lets us induce informative soft clustering and mixture models. Empirical results on natural language processing datasets show that our technique performs comparably to popular word embedding algorithms.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>