Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation
Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation
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DOI：
10.1137/130946782
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发表时间：
2014-01-01
影响因子：
2.1
通讯作者：
Gillis, Nicolas
中科院分区：
文献类型：
--
作者：
Gillis, Nicolas
在本文中，我们提出了一种新的快速且鲁棒的递归算法，用于近可分离非负矩阵分解，一个特殊的非负盲源分离问题。该算法我们称为连续非负投影算法（SNPA），与流行的连续投影算法（SPA）密切相关，但在分解中利用了非负约束。我们证明 SNPA 比 SPA 更稳健，并且可以应用于更广泛的非负矩阵。这在一些合成数据集和真实世界的高光谱图像上得到了说明。
In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive projection algorithm (SPA) but takes advantage of the nonnegativity constraint in the decomposition. We prove that SNPA is more robust than SPA and can be applied to a broader class of nonnegative matrices. This is illustrated on some synthetic data sets and on a real-world hyperspectral image.