Computer Science ETDs

Author

Vamsi Potluru

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 Advisor

Hayes, Thomas

First Committee Member (Chair)

Hayes, Thomas

Second Committee Member

Calhoun, Vince

Third Committee Member

Lane, Terran

Fourth Committee Member

Pearlmutter, Barak

Share

COinS