ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Blind source separation based on power spectral density
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Hi-index | 0.00 |
In this paper, we propose a novel algorithm, "Power- ICA", for independent component analysis (ICA) that is analog of the power iteration for solving the eigenvalue problem of a matrix. In each iteration the updating of ICA matrix is fully-multiplicative, rather than the partly multiplicative and partly additive in the conventional learning algorithms. Therefore, this algorithm presents a new class of algorithm to the ICA algorithms. The cost function for algorithm is based on a diagonality of a non-linearized covariance matrix. One of desired features is that the algorithm does not include any pre-designated parameter such as the learning step size, which is promising for applications to ICA with unknown types of sources. We also give conditions for choices of the non-linear functions. Numerical results show the effectiveness of PowerICA.