Tensors and latent variable models

Abstract

In this paper we discuss existing and new connections between latent variable models from machine learning and tensors (multi-way arrays) from multilinear algebra. A few ideas have been developed independently in the two communities. However, there are still many useful but unexplored links and ideas that could be borrowed from one of the communities and used in the other.We will start our discussion from simple concepts such as independent variables and rank-1 matrices and gradually increase the difficulty. The final goal is to connect discrete latent tree graphical models to state of the art tensor decompositions in order to find tractable representations of probability tables of many variables.