Structured Low-Rank Approximation as Optimization on a Grassmann Manifold
published: Aug. 26, 2013, recorded: July 2013, views: 4472
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Many data modeling problems can be posed and solved as a structured low-rank approximation problem. Using the variable projection approach, the problem is reformulated as optimization on a Grassmann manifold. We compare local optimization methods based on different parametrizations of the manifold, including recently proposed penalty method and method of switching permutations. A numerical example of system identiﬁcation is provided.
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