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University of New Mexico
Matrix Factorization: Nonnegativity, Sparsity and Independence
Abstract
dc:description.abstractMatrix 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.
Degree
thesis:*- Name thesis:degree_name
- Computer Science
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Department of Computer Science
- Year
- 2014
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Potluru, Vamsi
- Contributors dc:contributor
-
- Hayes, Thomas
- Calhoun, Vince
- Lane, Terran
- Pearlmutter, Barak
Subjects
dc:subject × 4Rights
- Language dc:language
- English
Identifiers
dc:identifier.*- Identifier
- https://digitalrepository.unm.edu/cs_etds/41
- OAI identifier oai:identifier
- oai:digitalrepository.unm.edu:cs_etds-1040