Go to content
UR Home

On model identifiability in analytic postnonlinear ICA

Theis, Fabian J. ; Gruber, P.


An important aspect of successfully analyzing data with blind source separation is to know the indeterminacies of the problem, that is how the separating model is related to the original mixing model. If linear independent component analysis (ICA) is used, it is well-known that the mixing matrix can be found in principle, but for more general settings not many results exist. In this work, only ...


Owner only: item control page
  1. Homepage UR

University Library

Publication Server


Publishing: oa@ur.de
0941 943 -4239 or -69394

Dissertations: dissertationen@ur.de
0941 943 -3904

Research data: datahub@ur.de
0941 943 -5707

Contact persons