A Sparse Decomposition of Low Rank Symmetric Positive Semidefinite Matrices
نویسندگان
چکیده
منابع مشابه
A Sparse Decomposition of Low Rank Symmetric Positive Semidefinite Matrices
Suppose that A ∈ RN×N is symmetric positive semidefinite with rank K ≤ N . Our goal is to decompose A into K rank-one matrices ∑K k=1 gkg T k where the modes {gk} K k=1 are required to be as sparse as possible. In contrast to eigen decomposition, these sparse modes are not required to be orthogonal. Such a problem arises in random field parametrization where A is the covariance function and is ...
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متن کاملA Sparse Decomposition of Low Rank Symmetric Positive Semidefinite Matrices | Multiscale Modeling & Simulation | Vol. 15, No. 1 | Society for Industrial and Applied Mathematics
Abstract. Suppose that A ∈ RN×N is symmetric positive semidefinite with rank K ≤ N . Our goal is to decompose A into K rank-one matrices ∑K k=1 gkg T k where the modes {gk} K k=1 are required to be as sparse as possible. In contrast to eigendecomposition, these sparse modes are not required to be orthogonal. Such a problem arises in random field parametrization where A is the covariance functio...
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ژورنال
عنوان ژورنال: Multiscale Modeling & Simulation
سال: 2017
ISSN: 1540-3459,1540-3467
DOI: 10.1137/16m107760x