sklearn.linear_model.orthogonal_mp(X, y, n_nonzero_coefs=None, tol=None, precompute=False, copy_X=True, return_path=False, return_n_iter=False)
[source]
Orthogonal Matching Pursuit (OMP)
Solves n_targets Orthogonal Matching Pursuit problems. An instance of the problem has the form:
When parametrized by the number of nonzero coefficients using n_nonzero_coefs
: argmin y  Xgamma^2 subject to gamma_0 <= n_{nonzero coefs}
When parametrized by error using the parameter tol
: argmin gamma_0 subject to y  Xgamma^2 <= tol
Read more in the User Guide.
Parameters: 


Returns: 

See also
OrthogonalMatchingPursuit
, orthogonal_mp_gram
, lars_path
, decomposition.sparse_encode
Orthogonal matching pursuit was introduced in S. Mallat, Z. Zhang, Matching pursuits with timefrequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 33973415. (http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf)
This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the KSVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report  CS Technion, April 2008. http://www.cs.technion.ac.il/~ronrubin/Publications/KSVDOMPv2.pdf
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.linear_model.orthogonal_mp.html