Embedding and latent variable models using maximal correlation
Name
986497419-MIT.pdf
Description
Full printable version
Size
547.55 KB
Format
Adobe PDF
Checksum (MD5)
6a9aaae60b3c97fe6a837dac47473b18
Author(s)
Qiu, David.
Advisor(s)
Lizhong Zheng.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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