ABSTRACT In this paper, we present an efficient method for nonnegative matrix factorization (NMF) based on the alternating nonnegative least-squares framework. To solve the nonnegativity constrained least-squares problems efficiently, we propose an extrapolated quadratic regularization projected Barzilai–Borwein (EQRPBB) method utilizing the extrapolation technique and a modified nonmonotone line search. The efficiency of the proposed method is demonstrated through experiments on synthetic and image datasets. We observe that our method significantly outperform existing ones in terms of computational speed.

中文翻译：

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一种用于外推非负矩阵分解的有效非单调投影 Barzilai-Borwein 方法

摘要 在本文中，我们提出了一种基于交替非负最小二乘框架的非负矩阵分解 (NMF) 的有效方法。为了有效地解决非负约束最小二乘问题，我们提出了一种利用外推技术和改进的非单调线搜索的外推二次正则化投影 Barzilai-Borwein (EQRPBB) 方法。通过对合成和图像数据集的实验证明了所提出方法的效率。我们观察到我们的方法在计算速度方面明显优于现有方法。