Improved method of solving the parameters of independent component analysis model and its application

A traditional ICA is a noise-free model and has some rigorous conditions. However, in fact, there are many noises, and people don’t know the relations between the number of sources and the number of observations. In the paper, the authors make full use of the theory of mean field approximation (MFA) in statistical physics to estimate the model parameters. In order to obtain more independent features from extraction, a scheme by adding some restrictions to model parameters, such as non-negative mixing matrices and non-negative source signals is proposed. Experiments have been done for several different cases, such as, speech signals, simulated face graphics and ORL face recognition. The over-complete case can also be separated well in speech signals experiment by the proposed method. The simulated graphics experimental results show this proposed method can extracted more independent face features, and the recognition ratio can be improved from 85% to 95% in the ORL face recognition experiment.