example of MCVQ trained on faces


Many collections of data exhibit a common underlying structure: they consist of a number of parts or factors, each with a range of possible states. When data are represented as vectors, parts manifest themselves as subsets of the data dimensions that take on values in a coordinated fashion.

In this project, we propose a form of probabilistic model for simultaneously learning both the parts and the states (or range of apperances) of each part. We provide details for two of these models: Multiple Cause Vector Quantization (MCVQ, pictured on the right) learns a discrete model of states, and Multiple Cause Factor Analysis (MCFA) learns a continuous model.

These models can be applied to learning the parts of objects (such as faces) in images, and collaborative filtering of movie ratings. For an introduction to MCVQ and MCFA, please see our JMLR article linked below.




Performance Results: