Parameter-wise co-clustering for high-dimensional data
This work addresses dimensionality reduction for data analysis, but it appears incremental as it builds on traditional co-clustering with added flexibility.
The paper tackles the problem of high-dimensional data by proposing a parameter-wise co-clustering model for continuous random variables, which maintains high parsimony while allowing more flexibility, and demonstrates its performance through comparisons with traditional co-clustering on simulated and real datasets.
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.