论文标题
将2D HSQC NMR混合物与$β$ -NMF和稀疏混合在一起
Unmixing 2D HSQC NMR mixtures with $β$-NMF and sparsity
论文作者
论文摘要
核磁共振（NMR）光谱是一种有效的技术，用于分析化学混合物，其中人们沿着一个矿石获得了更大的尺寸的化学混合物光谱。重要的问题之一是有效地分析混合物的组成，这是经典的盲源分离（BSS）问题。 NMR光谱的分辨率差及其较大的尺寸要求使用量身定制的BSS方法。我们在本文中提出了一种基于$β$ - 差异数据保真度的新的BSS变异配方，并结合了促进正则化功能的稀疏性。制定了一种大规模的最小化策略来解决该问题和实验，对模拟和实际的2D HSQC NMR数据说明了提出方法的兴趣和有效性。
Nuclear Magnetic Resonance (NMR) spectroscopy is an efficient technique to analyze chemical mixtures in which one acquires spectra of the chemical mixtures along one ore more dimensions. One of the important issues is to efficiently analyze the composition of the mixture, this is a classical Blind Source Separation (BSS) problem. The poor resolution of NMR spectra and their large dimension call for a tailored BSS method. We propose in this paper a new variational formulation for BSS based on a $β$-divergence data fidelity term combined with sparsity promoting regularization functions. A majorization-minimization strategy is developped to solve the problem and experiments on simulated and real 2D HSQC NMR data illustrate the interest and the effectiveness of the proposed method.
