Abstract

Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules of Boolean algebra. Here, we present its first probabilistic treatment. We facilitate s able sampling-based posterior inference by exploitation of the combinatorial structure of the factor conditionals. Maximum a posteriori decompositions feature higher accuracies than existing techniques throughout a wide range of simulated conditions. Moreover, the probabilistic approach facilitates the treatment of missing data and enables model selection with much greater accuracy. We investigate three real-world data-sets. First, temporal interaction networks in a hospital ward and behavioural data of university students demonstrate the inference of instructive latent pa terns. Next, we decompose a tensor with more than 10 billion data points, indicating relations gene expression in cancer patients. Not only does this demonstrate scalability, it also provides an entirely novel perspective on relational propertie of continuous data and, in the present example, on the molecular heterogeneity of cancer. Our implementation is available on GitHub2 .