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Active set type algorithms for nonnegative matrix factorization in hyperspectral unmixing. (English) Zbl 1435.90132

Summary: Hyperspectral unmixing is a powerful method of the remote sensing image mining that identifies the constituent materials and estimates the corresponding fractions from the mixture. We consider the application of nonnegative matrix factorization (NMF) for the mining and analysis of spectral data. In this paper, we develop two effective active set type NMF algorithms for hyperspectral unmixing. Because the factor matrices used in unmixing have sparse features, the active set strategy helps reduce the computational cost. These active set type algorithms for NMF is based on an alternating nonnegative constrained least squares (ANLS) and achieve a quadratic convergence rate under the reasonable assumptions. Finally, numerical tests demonstrate that these algorithms work well and that the function values decrease faster than those obtained with other algorithms.

MSC:

90C30 Nonlinear programming
65K05 Numerical mathematical programming methods
65F99 Numerical linear algebra

Software:

NeNMF
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Full Text: DOI

References:

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