Brendan J. Frey, Anitha Kannan, Nebojsa Jojic
Factor analysis and principal components analysis can be used to model linear relationships between observed variables and linearly map high-dimensional data to a lower-dimensional hidden space. In factor analysis, the observations are modeled as a linear com(cid:173) bination of normally distributed hidden variables. We describe a nonlinear generalization of factor analysis, called "product analy(cid:173) sis", that models the observed variables as a linear combination of products of normally distributed hidden variables. Just as fac(cid:173) tor analysis can be viewed as unsupervised linear regression on unobserved, normally distributed hidden variables, product anal(cid:173) ysis can be viewed as unsupervised linear regression on products of unobserved, normally distributed hidden variables. The map(cid:173) ping between the data and the hidden space is nonlinear, so we use an approximate variational technique for inference and learn(cid:173) ing. Since product analysis is a generalization of factor analysis, product analysis always finds a higher data likelihood than factor analysis. We give results on pattern recognition and illumination(cid:173) invariant image clustering.