Parameter-Wise Co-Clustering for High-Dimensional Data

Michael P B Gallaugher (1) , Christophe Biernacki (2) , Paul D Mcnicholas (1)

1 McMaster University [Hamilton, Ontario]
2 MODAL - MOdel for Data Analysis and Learning

In recent years, data dimensionality has increasingly become a concern, leading to many parameter and dimension reduction techniques being proposed in the litera- ture. A parameter-wise co-clustering model, for data modelled via continuous random variables, is presented. The proposed model, although allowing more flexibility, still maintains the very high degree of parsimony achieved by traditional co-clustering. A stochastic expectation-maximization (SEM) algorithm along with a Gibbs sampler is used for parameter estimation and an integrated complete log-likelihood criterion is used for model selection. Simulated and real datasets are used for illustration and comparison with traditional co-clustering.

Domaines: Statistiques [stat]; Méthodologie [stat.ME]

Michael P B Gallaugher, Christophe Biernacki, Paul D Mcnicholas. Parameter-Wise Co-Clustering for High-Dimensional Data. 2020. ⟨hal-01862824v3⟩