University of Illinois Urbana-Champaign

Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization

Lorenz, Florian M.

Content Files
1_Lorenz_Florian.pdf

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

Description

Title
Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization
Author(s)
Lorenz, Florian M.
Issue Date
2010-05-19T18:40:22Z
Director of Research (if dissertation) or Advisor (if thesis)
  • Hubert, Lawrence J.
  • Hong, Sungjin
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.A.
Degree Level
Thesis
Date of Ingest
2010-05-19T18:40:22Z
Keyword(s)
  • Nonnegative Matrix Factorization (NMF)
  • Sequential Fitting (SEFIT)
  • Alternating Least Squares (ALS)
  • Nonnegative Least Squares (NNLS)
Abstract
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.
Graduation Semester
2010-5
Permalink
http://hdl.handle.net/2142/16196
Copyright and License Information
Copyright 2010 Florian Markus Lorenz. All rights reserved.

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