A fast algorithm for estimating overcomplete ICA bases for image windows

We introduce a very fast method for estimating over-complete 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, over-complete set of vectors that are almost orthogonal to each other. Thus we may estimate an over-complete 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.