Parameter-wise co-clustering for high-dimensional data.

Autor:	Gallaugher, M. P. B., Biernacki, C., McNicholas, P. D.
Předmět:	GIBBS sampling RANDOM variables PARAMETER estimation EXPECTATION-maximization algorithms DATA modeling PARSIMONIOUS models
Zdroj:	Computational Statistics; Sep2023, Vol. 38 Issue 3, p1597-1619, 23p
Abstrakt:	In recent years, data dimensionality has increasingly become a concern, leading to many parameter and dimension reduction techniques being proposed in the literature. A parameter-wise co-clustering model, for (possibly high-dimensional) data modelled via continuous random variables, is presented. The proposed model, although allowing more flexibility, still maintains the very high degree of parsimony and interpretability achieved by traditional co-clustering. More precisely, the keystone consists of dramatically increasing the number of column-clusters while expressing each as a combination of a limited number of mean-dependent and variance-dependent column-clusters. A stochastic expectation-maximization 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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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