A recursive sparse blind source separation method for nonnegative and correlated data in NMR spectroscopy Conference

Sun, Y, Xin, J. (2011). A recursive sparse blind source separation method for nonnegative and correlated data in NMR spectroscopy . Lecture Notes in Computer Science, 6855 LNCS(PART 2), 81-88. 10.1007/978-3-642-23678-5_8

cited authors

  • Sun, Y; Xin, J

abstract

  • 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. A major approach to non-negative BSS relies on a strict non-overlap condition (also known as the pixel purity assumption in hyper-spectral imaging) of source signals which is not always guaranteed in the NMR spectra of chemical compounds. A new dominant interval condition is proposed. Each source signal dominates some of the other source signals in a hierarchical manner. The rBSS method then reduces the BSS problem into a series of sub-BSS problems by a combination of data clustering, linear programming, and successive elimination of variables. In each sub-BSS problem, an ℓ1 minimization problem is formulated for recovering the source signals in a sparse transformed domain. The method is substantiated by NMR data. © 2011 Springer-Verlag.

authors

publication date

  • September 20, 2011

published in

Digital Object Identifier (DOI)

start page

  • 81

end page

  • 88

volume

  • 6855 LNCS

issue

  • PART 2