Paper No: 152
Identifiability conditions and subspace clustering in sparse BSS
Author(s): Pando Georgiev, Fabian Theis, Anca Ralescu
Abstract
We give general identifiability conditions on the source matrix in
Blind Signal Separation problem. They refine some previously known ones.
We develop a subspace clustering algorithm, which is a generalization of the
$k$-plane clustering algorithm, and is suitable for separation of sparse
mixtures with bigger sparsity (i.e. when the number of the sensors
is bigger at least by 2 than the number of non-zero elements in most of the columns
of the
source matrix). We demonstrate our algorithm by examples
in the square and underdetermined cases. The latter confirms the new identifiability
conditions
which require less hyperplanes in the data for full recovery of the sources
and the
mixing matrix. |
