Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Monte Carlo Algorithm for Least Dependent Non-Negative Mixture Decomposition
 

Summary: Monte Carlo Algorithm for Least Dependent
Non-Negative Mixture Decomposition
Sergey A. Astakhov,*, Harald Sto1gbauer,, Alexander Kraskov,, and Peter Grassberger
John von Neumann Institute for Computing, Forschungszentrum Ju¨lich, D-52425, Ju¨lich, Germany, and Division of Biology,
California Institute of Technology, Pasadena, California 91125
We propose a simulated annealing algorithm (stochastic
non-negative independent component analysis, SNICA)
for blind decomposition of linear mixtures of non-negative
sources with non-negative coefficients. The demixing is
based on a Metropolis-type Monte Carlo search for least
dependent components, with the mutual information
between recovered components as a cost function and
their non-negativity as a hard constraint. Elementary
moves are shears in two-dimensional subspaces and
rotations in three-dimensional subspaces. The algorithm
is geared at decomposing signals whose probability densi-
ties peak at zero, the case typical in analytical spectro-
scopy and multivariate curve resolution. The decomposi-
tion performance on large samples of synthetic mixtures
and experimental data is much better than that of tradi-

  

Source: Astakhov, Sergey - John von Neumann Institute for Computing, Forschungszentrum Jülich

 

Collections: Computer Technologies and Information Sciences; Physics