rs-Sparse principal component analysis: A mixed integer nonlinear programming approach with VNS

Principal component analysis is a popular data analysis dimensionality reduction technique, aiming to project with minimum error for a given dataset into a subspace of smaller number of dimensions. In order to improve interpretability, different variants of the method have been proposed in the liter...

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Veröffentlicht in:	Computers & operations research Jg. 52; H. Part B; S. 349 - 354
Hauptverfasser:	Carrizosa, Emilio, Guerrero, Vanesa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.12.2014 Pergamon Press Inc
Schlagworte:	Errors Heuristic Integer programming Mathematical models Mathematical problems Mixed integer Nonlinear mixed integer programming Nonlinear programming Numerical analysis Operations research Principal component analysis Principal components analysis Searching Sparse principal component analysis Sparsity Studies Subspaces Variable neighborhood search Nonlinear mixed integer programming Sparse principal component analysis Variable neighborhood search
ISSN:	0305-0548, 1873-765X, 0305-0548
Online-Zugang:	Volltext
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