Linear Multilayer Ica Algorithm Integrating Small Local Modules

In this paper, the linear (feed-forward) multilayer ICA algorithm is proposed for the blind separation of high-dimensional mixed signals. There are two main phases in each layer. One is the local ICA phase, where the mixed signals are divided into small local modules and a simple ICA is applied to each module. Another is the mapping phase, where the locally-separated signals are arranged as a line so that the higher correlated signals are nearer. By repetition of these two phase, this multilayer ICA algorithm can find all the (global) independent components through only the ICA processing on local modules. Some numerical experiments on artificial data and natural scenes show the validity of this algorithm, and verify that it is more efficient than the standard fast ICA algorithm for “locally-biased” and highdimensional observed signals such as natural scenes.