Estimation of Gaussian Bi-Clusters with General Block-Diagonal Covariance Matrix and Applications

Abstract

Bi-clustering is a technique that allows for the simultaneous clustering of observations and features in a dataset. This technique is often used in bioinformatics, text mining, and time series analysis. An important advantage of biclustering algorithm is the ability to uncover multiple ``views'' (i.e., through rows and column groupings) in the data. Several Gaussian mixture model based biclustering approach currently exist in the literature. However, they impose severe restrictions on the structure of the covariance matrix. Here, we propose a Gaussian mixture model-based bi-clustering approach that provides a more flexible block-diagonal covariance structure. We show that the clustering accuracy of the proposed model is comparable to other known techniques but our approach provides a more flexible covariance structure and has substantially lower computational time. We demonstrate the application of the proposed model in bioinformatics and topic modelling.