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Nonnegative matrix factorization (NMF) is a popular approach to model data, however, most models are unable to flexibly take into account multiple matrices across sources and time or apply only to integer-valued data. We introduce a probabilistic, Gaussian Process-based, more inclusive NMF-based model which jointly analyzes nonnegative data such as text data word content from multiple sources in a temporal dynamic manner. The model collectively models observed matrix data, source-wise latent variables, and their dependencies and temporal evolution with a full-fledged hierarchical approach including flexible nonparametric temporal dynamics. Experiments on simulated data and real data show the model out-performs, comparable models. A case study on social media and news demonstrates the model discovers semantically meaningful topical factors and their evolution

Probabilistic Dynamic Non-negative Group Factor Model for Multi-source Text Mining