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Fast Algorithms for Informed Source Separation
This video was recorded at International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS): theory and applications, Leuven 2013. We study a convex formulation of low-rank matrix factorization, in a special case where additional information on the factors is known. Our formulation is typically adapted to source separation scenarii, where additional information on the sources may be provided by an expert. Our formulation promotes low-rank with a nuclear-norm based penalty. As it is non-smooth, generic first-order algorithms suffer from slow convergence rates. We study and compare several algorithms that fully exploit the structure of our problem while keeping memory requirements linear in the size of the problem.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
