TensOrMachine: Probabilistic Boolean Tensor Decomposition

Understand

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 scalable 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.