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Description
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 |
Discipline: | Psychology |
Degree Granting Institution: | University of Illinois at Urbana-Champaign |
Degree: | M.A. |
Genre: | Thesis |
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 |
URI: | http://hdl.handle.net/2142/16196 |
Rights Information: | Copyright 2010 Florian Markus Lorenz. All rights reserved. |
Date Available in IDEALS: | 2010-05-19 |
Date Deposited: | May 2010 |
This item appears in the following Collection(s)
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Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois -
Dissertations and Theses - Psychology
Dissertations and Theses from the Dept. of Psychology