Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization
نویسندگان
چکیده
منابع مشابه
Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) under the separability assumption can provably be solved efficiently, even in the presence of noise, and has been shown to be a powerful technique in document classification and hyperspectral unmixing. This problem is referred to as near-separable NMF and requires that there exists a cone spanned by a small subset of the columns of the input nonnegative ma...
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Nonnegative Matrix Factorization (NMF) solves the following problem: find such nonnegative matrices A ∈ RI×J + and X ∈ RJ×K + that Y ∼= AX, given only Y ∈ RI×K and the assigned index J (K >> I ≥ J). Basically, the factorization is achieved by alternating minimization of a given cost function subject to nonnegativity constraints. In the paper, we propose to use Quadratic Programming (QP) to solv...
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Article history: Received 16 September 2007 Received in revised form 1 July 2008 Accepted 16 September 2008 Available online 5 November 2008
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ژورنال
عنوان ژورنال: SIAM Journal on Optimization
سال: 2015
ISSN: 1052-6234,1095-7189
DOI: 10.1137/130940670