A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices
提出一种新的潜在分块模型，用于高稀疏数据矩阵的动态共聚类，通过零膨胀分布和常微分方程建模时变稀疏性和聚类演变，并用神经网络求解，适用于计数数据。
The simultaneous clustering of observations and features of datasets (known as co-clustering) has recently emerged as a central machine learning application to summarize massive datasets. However, most existing models focus on continuous and dense data in stationary scenarios, where cluster assignments do not evolve over time. This work introduces a novel latent block model for the dynamic co-clustering of data matrices with high sparsity. To properly model this type of data, we assume that the observations follow a time and block dependent mixture of zero-inflated distributions, thus, combining stochastic processes with the time-varying sparsity modeling. To detect abrupt changes in the dynamics of both cluster memberships and data sparsity, the mixing and sparsity proportions are modeled through systems of ordinary differential equations. The inference relies on an original variational procedure whose maximization step trains fully connected neural networks in order to solve the dynamical systems. Numerical experiments on simulated and real world datasets demonstrate the effectiveness of the proposed methodology in the context of count data.