Separation capability of overcomplete ICA approaches
Markus Borschbach, Imke Hahn · 2007
Abstract: The Relaxation to have only square matrices in standard ICA leads to an approximation of the inverse of non-quadratic matrices to determine the separation matrix. Synthetic data sets as well as speech data are used to compare the capability of such approaches, called overcomplete ICA on an underdetermined basis. Due to the fact that the mixing matrices are not invertible (because they are not square), the quality of the sources ’ reconstruction is not excellent. The most extreme case of an undetermined ICA is single channel ICA. But in this paper not the reduction to one sensor is considered but in a maximum case the reduction from eight sensors to two sensor signals. It is shown, which separation quality can still be achieved for the blind separation of the underlying sources. For an improved classification the algorithms are also compared to well-known standard ICA-algorithms.