Computer Science ETDs
Publication Date
5-1-2014
Abstract
Matrix factorization arises in a wide range of application domains and is useful for extracting the latent features in the dataset. Examples include recommender systems, brain data analysis, and document clustering. In this dissertation, we are interested in matrix factorizations which impose the requirements of nonnegativity, sparsity or independence.
Language
English
Keywords
Matrix factorization, Nonnegativity, Sparsity, Independence
Document Type
Dissertation
Degree Name
Computer Science
Level of Degree
Doctoral
Department Name
Department of Computer Science
First Committee Member (Chair)
Thomas Hayes
Second Committee Member
Vince Calhoun
Third Committee Member
Terran Lane
Fourth Committee Member
Barak Pearlmutter
Recommended Citation
Potluru, Vamsi. "Matrix Factorization: Nonnegativity, Sparsity and Independence." (2014). https://digitalrepository.unm.edu/cs_etds/41
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