Parameter-wise co-clustering for high-dimensional data

TL;DR

This paper introduces a flexible parameter-wise co-clustering model for high-dimensional continuous data, utilizing SEM and Gibbs sampling for estimation, and demonstrates its effectiveness through simulations and real data comparisons.

Contribution

It presents a novel, more flexible co-clustering approach for continuous data that maintains parsimony and employs SEM and Gibbs sampling for parameter estimation.

Findings

The model performs well on simulated datasets.

It outperforms traditional co-clustering methods.

Effective for high-dimensional continuous data.

Abstract

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 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.