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Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms

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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.

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Authors and Affiliations

  1. Faculty of Psychology and Educational Sciences, KU Leuven, Andreas Vesaliusstraat 2, Box 3762, B-3000, Leuven, Belgium

    Tom F. Wilderjans & Iven Van Mechelen

  2. suAzio Consulting, Antwerp, Belgium

    Dirk Depril

Authors
  1. Tom F. Wilderjans
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  2. Dirk Depril
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  3. Iven Van Mechelen
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Correspondence to Tom F. Wilderjans.

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Wilderjans, T.F., Depril, D. & Van Mechelen, I. Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms. J Classif 30, 56–74 (2013). https://doi.org/10.1007/s00357-013-9120-0

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