Editing Signal separation (section)

== Approaches ==
Since the chief difficulty of the problem is its underdetermination, methods for blind source separation generally seek to narrow the set of possible solutions in a way that is unlikely to exclude the desired solution. In one approach, exemplified by [[principal components analysis|principal]] and [[independent components analysis|independent]] component analysis, one seeks source signals that are minimally [[correlation|correlated]] or maximally [[independence (probability)|independent]] in a probabilistic or [[information theory|information-theoretic]] sense. A second approach, exemplified by [[nonnegative matrix factorization]], is to impose structural constraints on the source signals. These structural constraints may be derived from a generative model of the signal, but are more commonly heuristics justified by good empirical performance. A common theme in the second approach is to impose some kind of low-complexity constraint on the signal, such as [[sparsity]] in some [[basis (linear algebra)|basis]] for the signal space. This approach can be particularly effective if one requires not the whole signal, but merely its most salient features.

=== Methods ===
There are different methods of blind signal separation:

* [[Principal components analysis]]
* [[Singular value decomposition]]
* [[Independent component analysis]]<ref name="cj">P. Comon and C. Jutten (editors). “Handbook of Blind Source Separation, Independent Component Analysis and Applications” Academic Press, {{ISBN|978-2-296-12827-9}}</ref><ref>Shlens, Jonathon. "A tutorial on independent component analysis." {{ArXiv|1404.2986}}</ref>
* [[Dependent component analysis]]
* [[Non-negative matrix factorization]]
* [[Low-complexity coding and decoding]]
* [[Stationary subspace analysis]]
* [[Common spatial pattern]]
* [[Canonical correlation analysis]]