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

Authors:
Yuanchang Sun;Jack Xin
Affiliations:
Math Department, Univ of California Irvine, Irvine, CA;Math Department, Univ of California Irvine, Irvine, CA
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Year:
2011

Citing 3
Cited 0

Blind source separation of positive and partially correlated data

Signal Processing
Hyperspectral Data Exploitation: Theory and Applications

Hyperspectral Data Exploitation: Theory and Applications
Postprocessing and sparse blind source separation of positive and partially overlapped data

Signal Processing

Quantified Score

Hi-index	0.00

Visualization

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 nonoverlap condition (also known as the pixel purity assumption in hyperspectral 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.