Aapo Hyvärinen, Razvan Cristescu and Erkki Oja. A fast algorithm for estimating overcomplete ICA bases for image windows. To appear in Proc. Int. Joint Conf on Neural Networks, Washington D.C., 1999.
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Abstract: We introduce a very fast method for estimating overcomplete bases of independent components from image data. This is based on the concept of quasi-orthogonality, which means that in a very high-dimensional space, there can be a large, overcomplete set of vectors that are almost orthogonal to each other. Thus we may estimate an overcomplete basis by using one-unit ICA algorithms and forcing only partial decorrelation between the different independent components. The method can be implemented using a modification of the FastICA algorithm, which leads to a computationally highly efficient method.