Sparse BSS with GMCALab


GMCALab is a set of Matlab toolboxes that focus on solving Blind Source Separation problems from multichannel/multispectral/hyperspectral data. In essence, multichannel data provide different observations of the same physical phenomena (e.g. multiple wavelengths, ), which are modeled as a linear combination of unknown elementary components or sources:

\mathbf{Y} = \mathbf{A}\mathbf{S},

where \mathbf{Y} is the data matrix, \mathbf{S} is the source matrix, and \mathbf{A} is the mixing matrix. The goal of blind source separation is to retrieve \mathbf{A} and \mathbf{S} from the knwoledge of the data only.

Generalized Morphological Component Analysis, a.k.a. GMCA, is a BSS method that enforces the sparsity of the sought-after sources:


A lightweight Matlab/Octave version of the GMCALab toolbox is available at this location. Illustrations are provide here.

It is worth noting that GMCA provides a very generic framework that has been extended to tackle different matrix factorization problems:

  • Non-negative matrix factorization with nGMCA
  • Separation of partially correlated sources with AMCA
  • The decomposition of hyperspectral data with HypGMCA (available soon)
  • The analysis of multichannel data in the presence of outliers with rAMCA at this location (updated the 14/06/16).

 We are now developping a python-based toolbox coined pyGMCALab, which is available at this location.