  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.
