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

Tom F. Wilderjans, Dirk Depril, Iven van Mechelen

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)56-74
Number of pages19
JournalJournal of Classification
Volume30
Issue number1
DOIs
Publication statusPublished - 11 Jan 2013
Externally publishedYes

Keywords

  • Additive clustering
  • ALS algorithms
  • Biclustering
  • Co-clustering
  • PENCLUS
  • Simulation study
  • Simultaneous overlapping clusterings
  • Two-mode clustering
  • Two-mode data

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