Abstract
This paper addresses the problem of on-line Independent Component Analysis (ICA). This area has been explosively investigated, and nowadays, there have already been a great many algorithms. However, a serious problem is that most of the existing algorithms can only work as batch processing, since they usually include some ensemble expectation operations. In this paper, we propose an on-line ICA algorithm that is performed as recursive processing on a sequential finite lengthened input blocks, for learning the mixing parameters and for separating the sources based on a maximization of non-Gaussianity of the outputs. Numerical experiments show that the proposed algorithm can converge efficiently and can separate sources appropriately.
