Variational phasor mean field model for object recognition

The variational phasor mean field model (VPMF) for Markov random fields can well represent marginal distribution as well as correlation among the sites. The network is represented by complex equations, which consist of phase equations and variational mean-field equations; thus the VPMF enables not only to improve the accuracy of the mean field approximation but also to give additional correlational relation between units with the cosine of the phase differences. In this report we discuss VPMF as an object recognition tool, and show that it provides efficient learning methods through computer experiments.