Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization

Nonnegative matrix factorization (NMF) and nonnegative least squares regression (NNLS regression) are widely used in the physical sciences; this thesis explores the often-overlooked origins of NMF in the psychometrics literature. Another method originating in psychometrics is sequentially-fit factor analysis (SEFIT). SEFIT was used to provide faster solutions to NMF, using both alternating least squares (ALS) with zero-substitution of negative values and NNLS. In a simulation using SEFIT for NMF, differences in fit between the ALS-based solution and the NNLS-based solution were minimal; both solutions were substantially faster than standard whole matrix based approaches to NMF.

Name thesis:degree_name

M.A.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2010

Author dc:creator

• Lorenz, Florian M.

Contributors dc:contributor

• Hubert, Lawrence J.
• Hong, Sungjin

• Nonnegative Matrix Factorization (NMF)
• Sequential Fitting (SEFIT)
• Alternating Least Squares (ALS)
• Nonnegative Least Squares (NNLS)

Statement dc:rights

• Copyright 2010 Florian Markus Lorenz. All rights reserved.

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/16196

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/16196