Maximum entropy ICA constrained by individual entropy maximization employing self-organizing maps
Noriaki Suetake, Yoshihiko Nakamura, T. Yamakawa · 2003
We propose new method of the independent component analysis (ICA), which doesn't require to know the classes of the distribution of the sources beforehand in contrast with conventional methods and achieves separation of sources with higher precision than the conventional methods. The proposed method employs the self-organizing maps (SOM) to the Bell-Sejnowski's method (1995) for the purpose of making use of the ability of SOM to approximate the probability density. SOM grasps the probability density of the input signals by nature. It can also track the changes of the probability density of the input signal adaptively. In this paper, the effectiveness and validity of the proposed method are verified by applying it to the separation of linearly mixed sounds and linearly mixed pictures by the computer simulations.