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Abstract:
Stochastic meta-descent (SMD) is a new technique for online
adaptation of local learning rates in arbitrary
twice-differentiable systems. Like matrix momentum it uses full
second-order information while retaining O(n) computational
complexity by exploiting the efficient computation of
Hessian-vector products. Here we apply SMD to independent component
analysis, and employ the resulting algorithm for the blind
separation of time-varying mixtures. By matching individual
learning rates to the rate of change in each source signal's
mixture coefficients, our technique is capable of simultaneously
tracking sources that move at very different, a priori unknown
speeds.
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