Multilinear Dirichlet Processes
This addresses the need for better modeling of multi-factor interactions in machine learning, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of modeling data with heterogeneous relationships from multiple factors by proposing MultiLinear Dirichlet Processes (MLDP), which combines Dirichlet processes with multilinear factor analyzers, achieving state-of-the-art performance on real-world datasets.
Dependent Dirichlet processes (DDP) have been widely applied to model data from distributions over collections of measures which are correlated in some way. On the other hand, in recent years, increasing research efforts in machine learning and data mining have been dedicated to dealing with data involving interactions from two or more factors. However, few researchers have addressed the heterogeneous relationship in data brought by modulation of multiple factors using techniques of DDP. In this paper, we propose a novel technique, MultiLinear Dirichlet Processes (MLDP), to constructing DDPs by combining DP with a state-of-the-art factor analysis technique, multilinear factor analyzers (MLFA). We have evaluated MLDP on real-word data sets for different applications and have achieved state-of-the-art performance.