Parameter-Wise Co-Clustering for High-Dimensional Data

Gallaugher, Michael; Biernacki, Christophe; Mcnicholas, Paul

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1007/s00180-022-01289-2

Title :

Parameter-Wise Co-Clustering for High-Dimensional Data

Author(s) :

Gallaugher, Michael [Auteur]
Baylor University
Biernacki, Christophe [Auteur]

MOdel for Data Analysis and Learning [MODAL]
Mcnicholas, Paul [Auteur]
McMaster University [Hamilton, Ontario]

Journal title :

Computational Statistics

Publisher :

Springer Verlag

Publication date :

2022-10

ISSN :

0943-4062

HAL domain(s) :

Statistiques [stat]
Statistiques [stat]/Méthodologie [stat.ME]

English abstract : [en]

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 ...
Show more >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 (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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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