A recursive sparse blind source separation method and its application to correlated data in NMR spectroscopy of biofluids

Yuanchang Sun Jack Xin

Geometric Modeling and Processing mathscidoc:1912.43880

Journal of Scientific Computing, 51, (3), 733-753, 2012.6
Motivated by the nuclear magnetic resonance (NMR) spectroscopy of biofluids (urine and blood serum), we present a recursive blind source separation (rBSS) method for nonnegative and correlated data. BSS problem arises when one attempts to recover a set of source signals from a set of mixture signals without knowing the mixing process. Various approaches have been developed to solve BSS problems relying on the assumption of statistical independence of the source signals. However, signal independence is not guaranteed in many real-world data like the NMR spectra of chemical compounds. The rBSS method introduced in this paper deals with the nonnegative and correlated signals arising in NMR spectroscopy of biofluids. The statistical independence requirement is replaced by a constraint which requires dominant interval(s) from each source signal over some of the other source signals in a
No keywords uploaded!
[ Download ] [ 2019-12-24 21:04:10 uploaded by Jack_Xin ] [ 578 downloads ] [ 0 comments ]
@inproceedings{yuanchang2012a,
  title={A recursive sparse blind source separation method and its application to correlated data in NMR spectroscopy of biofluids},
  author={Yuanchang Sun, and Jack Xin},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210410855198444},
  booktitle={Journal of Scientific Computing},
  volume={51},
  number={3},
  pages={733-753},
  year={2012},
}
Yuanchang Sun, and Jack Xin. A recursive sparse blind source separation method and its application to correlated data in NMR spectroscopy of biofluids. 2012. Vol. 51. In Journal of Scientific Computing. pp.733-753. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210410855198444.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved