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Sparse Generalized Eigenvalue Problem Via Smooth Optimization
2015
IEEE Transactions on Signal Processing
In this paper, we consider an ℓ_0-norm penalized formulation of the generalized eigenvalue problem (GEP), aimed at extracting the leading sparse generalized eigenvector of a matrix pair. The formulation involves maximization of a discontinuous nonconcave objective function over a nonconvex constraint set, and is therefore computationally intractable. To tackle the problem, we first approximate the ℓ_0-norm by a continuous surrogate function. Then an algorithm is developed via iteratively
doi:10.1109/tsp.2015.2394443
fatcat:35x6gxlvwjhjhg6s26jninbrje