高光谱非负矩阵分解（R-CoNMF）
Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)
摘要：
最近推出的协作非负矩阵分解（CoNMF）算法的设想，同时估计的端元的数量，混合矩阵，和分数丰度从高光谱线性混合物。本文介绍了R-CoNMF，它是CoNMF的一个健壮版本。通过a）包括体积正则化器，其惩罚到由纯像素算法推断的混合矩阵的距离;以及通过b）引入新的邻近交替优化（PAO）算法，其保证收敛到临界点，增加了鲁棒性。我们的实验结果表明，R-CoNMF提供了有效的估计时，端元的数量是未知的，当他们是已知的。
英文摘要：
The recently introduced collaborative nonnegative matrix factorization (CoNMF) algorithm was conceived to simultaneously estimate the number of endmembers, the mixing matrix, and the fractional abundances from hyperspectral linear mixtures. This paper introduces R-CoNMF, which is a robust version of CoNMF. The robustness has been added by a) including a volume regularizer which penalizes the distance to a mixing matrix inferred by a pure pixel algorithm; and by b) introducing a new proximal alternating optimization (PAO) algorithm for which convergence to a critical point is guaranteed. Our experimental results indicate that R-CoNMF provides effective estimates both when the number of endmembers are unknown and when they are known.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
