Editing Independent component analysis (section)

=== Identifiability ===

The independent components are identifiable up to a permutation and scaling of the sources.<ref>Theorem 11, Comon, Pierre. "Independent component analysis, a new concept?." Signal processing 36.3 (1994): 287-314.</ref> This identifiability requires that:

* At most one of the sources <math>s_k</math> is Gaussian,
* The number of observed mixtures, <math>m</math>, must be at least as large as the number of estimated components <math>n</math>: <math>m \ge n</math>. It is equivalent to say that the mixing matrix <math>\boldsymbol{A}</math> must be of full [[rank (linear algebra)|rank]] for its inverse to exist.