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Hubert, Lawrence J. Hong, Sungjin Lorenz, Florian M. 2010-05-19T18:40:22Z 2010-05-19T18:40:22Z 2010-05-19T18:40:22Z 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2010-04-02T18:02:23Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Lorenz_Florian.pdf: 256809 bytes, checksum: d6b16d4fe8dd3be779b6306c8dbec6e6 (MD5) Made available in DSpace on 2010-05-19T18:40:22Z (GMT). No. of bitstreams: 3 Lorenz_Florian.pdf: 256809 bytes, checksum: d6b16d4fe8dd3be779b6306c8dbec6e6 (MD5) 1_Lorenz_Florian.pdf: 248257 bytes, checksum: eb88ce25f7b8ba26ab673e6eb9960dcf (MD5) license.txt: 4064 bytes, checksum: 65a4faaf9a901cf85f07d98d13c2bc9e (MD5) http://hdl.handle.net/2142/16196 en Copyright 2010 Florian Markus Lorenz. All rights reserved. Nonnegative Matrix Factorization (NMF) Sequential Fitting (SEFIT) Alternating Least Squares (ALS) Nonnegative Least Squares (NNLS) Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization 2010-5 Psychology 1299 Psychology 0338 University of Illinois at Urbana-Champaign Thesis M.A. PHD:Psychology -UIUC 10KS0338PHD