sklearn.manifold.smacof(dissimilarities, metric=True, n_components=2, init=None, n_init=8, n_jobs=None, max_iter=300, verbose=0, eps=0.001, random_state=None, return_n_iter=False)
[source]
Computes multidimensional scaling using the SMACOF algorithm.
The SMACOF (Scaling by MAjorizing a COmplicated Function) algorithm is a multidimensional scaling algorithm which minimizes an objective function (the stress) using a majorization technique. Stress majorization, also known as the Guttman Transform, guarantees a monotone convergence of stress, and is more powerful than traditional techniques such as gradient descent.
The SMACOF algorithm for metric MDS can summarized by the following steps:
The nonmetric algorithm adds a monotonic regression step before computing the stress.
Parameters: 


Returns: 

“Modern Multidimensional Scaling  Theory and Applications” Borg, I.; Groenen P. Springer Series in Statistics (1997)
“Nonmetric multidimensional scaling: a numerical method” Kruskal, J. Psychometrika, 29 (1964)
“Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis” Kruskal, J. Psychometrika, 29, (1964)
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Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.manifold.smacof.html