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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Considerations on Parallelizing Nonnegative Matrix Factorization for Hyperspectral Data Unmixing
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Considerations on Parallelizing Nonnegative Matrix Factorization for Hyperspectral Data Unmixing

机译：高光谱数据分解中并行非负矩阵分解的考虑

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摘要

Nonnegative matrix factorization (NMF) is a recently developed linear unmixing technique that assumes that the original sources and transform were positively defined. Given that the linear mixing model (LMM) for hyperspectral data requires positive endmembers and abundances, with only minor modifications, NMF can be used to solve LMM. Traditionally, NMF solutions include an iterative process resulting in considerable execution times. In this letter, we provide two novel algorithms aimed at speeding the NMF through parallel processing: the first based on the traditional multiplicative solution and the second modifying an adaptive projected gradient technique known to provide better convergence. The algorithms' implementations were tested on various data sets; the results suggest that a significant speedup can be achieved without decrease in accuracy. This supports the further use of NMF for linear unmixing.
机译：非负矩阵分解（NMF）是最近开发的线性分解技术，它假定原始源和变换均已得到正向定义。鉴于用于高光谱数据的线性混合模型（LMM）需要正端成员和丰度，而只需进行少量修改，就可以使用NMF求解LMM。传统上，NMF解决方案包括一个迭代过程，这会导致相当长的执行时间。在这封信中，我们提供了两种旨在通过并行处理提高NMF速度的新颖算法：第一种基于传统的乘法解决方案，第二种修改了已知可提供更好收敛性的自适应投影梯度技术。在各种数据集上测试了算法的实现；结果表明可以在不降低精度的情况下实现显着的加速。这支持将NMF进一步用于线性分解。

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