Real and complex independent subspace analysis by generalized variance
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Here, we address the problem of Independent Subspace Analysis (ISA). We develop a technique that (i) builds upon joint decorrelation for a set of functions, (ii) can be related to kernel based techniques, (iii) can be interpreted as a self-adjusting, self-grouping neural network solution, (iv) can be used both for real and for complex problems, and (v) can be a first step towards large scale problems. Our numerical examples extend to a few 100 dimensional ISA tasks.
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