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

作者：Wilderjans Tom F^*; Depril Dirk; Van Mechelen Iven

来源：Journal of Classification, 2013, 30(1): 56-74.

DOI：10.1007/s00357-013-9120-0

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

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.

• 出版日期2013-4