Nonnegative Sparse Blind Source Separation for NMR Spectroscopy by Data Clustering, Model Reduction, and \ell_1 Minimization

Yuanchang Sun Jack Xin

Geometric Modeling and Processing mathscidoc:1912.43879

SIAM Journal on Imaging Sciences, 5, (3), 886-911, 2012.7
Motivated by applications in nuclear magnetic resonance (NMR) spectroscopy, we introduce a novel blind source separation (BSS) approach to treat nonnegative and correlated data. We consider the (over)-determined case where n sources are to be separated from n linear mixtures (n). Among the n source signals, there are n partially overlapping (Po) sources and one positive everywhere (Pe) source. This condition is applicable for many real-world signals such as NMR spectra of urine and blood serum for metabolic fingerprinting and disease diagnosis. The geometric properties of the mixture matrix and the sparseness structure of the source signals (in a transformed domain) are crucial to the identification of the mixing matrix and the sources. The method first identifies the mixing coefficients of the Pe source by exploiting geometry in data clustering. Then subsequent elimination of variables leads to a sub
No keywords uploaded!
[ Download ] [ 2019-12-24 21:04:07 uploaded by Jack_Xin ] [ 513 downloads ] [ 0 comments ]
@inproceedings{yuanchang2012nonnegative,
  title={Nonnegative Sparse Blind Source Separation for NMR Spectroscopy by Data Clustering, Model Reduction, and \ell_1 Minimization},
  author={Yuanchang Sun, and Jack Xin},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210407314940443},
  booktitle={SIAM Journal on Imaging Sciences},
  volume={5},
  number={3},
  pages={886-911},
  year={2012},
}
Yuanchang Sun, and Jack Xin. Nonnegative Sparse Blind Source Separation for NMR Spectroscopy by Data Clustering, Model Reduction, and \ell_1 Minimization. 2012. Vol. 5. In SIAM Journal on Imaging Sciences. pp.886-911. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210407314940443.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved