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Title:Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization
Author(s):Lorenz, Florian M.
Advisor(s):Hubert, Lawrence J.; Hong, Sungjin
Department / Program:Psychology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(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.
Issue Date:2010-05-19
Rights Information:Copyright 2010 Florian Markus Lorenz. All rights reserved.
Date Available in IDEALS:2010-05-19
Date Deposited:May 2010

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