Title: Additive biclustering: A comparison of one new and two existing ALS algorithms
Authors: Wilderjans, Tom ×
Depril, Dirk
Van Mechelen, Iven #
Issue Date: 2013
Publisher: Published for the Classification Society of North America by Springer-Verlag New York
Series Title: Journal of Classification vol:30 issue:1 pages:56-74
Abstract: The additive biclustering model for two-way two-mode object by variable data implies overlapping clusterings of both the objects and the variables together with a weight for each bicluster (i.e., a pair of an object and a variable cluster). In the data analysis, an additive biclustering model is fitted to given data by means of minimizing a least squares loss function. To this end, two alternating least squares algorithms (ALS) may be used: (1) PENCLUS, and (2) Baier's ALS approach. However, both algorithms suffer from some inherent limitations, which may hamper their performance. As a way out, based on theoretical results regarding optimally designing ALS algorithms, in this paper a new ALS algorithm will be presented. In a simulation study this algorithm will be shown to outperform the existing ALS approaches.
ISSN: 0176-4268
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Methodology of Educational Sciences
Quantitative Psychology and Individual Differences
× corresponding author
# (joint) last author

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