  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.
