Deep dynamic co-clustering of streams of count data: a new online Zip-dLBM

Abstract

Co-clustering is a technique used to analyze complex and high-dimensional data in various fields. However, traditional co-clustering methods are usually limited to dense data sets and require massive amount of memory, which can be limiting in some applications. To address this issue, we propose an online co-clustering model that processes the data incrementally and introduces a novel latent block model for sparse data matrices. The proposed model employs a LSTM neural network and a time and block dependent mixture of zero-inflated distributions to model sparsity and aims to detect real-time changes in dynamics through Bayesian online change point detection. An original variational procedure is proposed for inference. Simulations demonstrate the effectiveness of the methodology for count data.