|Stochastic Systems Group|
In this talk, we explore how random matrix theory and free probability can help us analyze and develop signal processing algorithms that are robust under data constraints. We will provide an example where this theory dramatically outperforms the MDL/AIC based rank detection algorithms. We then characterize the general class of problems that may be modelled with this theory, and conclude with a demonstration of a `random matrix calculator' which transforms theory into practice.
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