Quadratic Programming Feature Selection
EntityUAM. Departamento de Ingeniería Informática
PublisherM I T Press
CitationJournal of Machine Learning Research 11 (2010): 1491-1516
ISSN1533-7928 (online); 1532-4435 (print)
Funded byI.R.-L. is supported by an FPU grant from Universidad Autónoma de Madrid, and partially supported by the Universidad Autónoma de Madrid-IIC Chair. R.H. acknowledges partial support by ONR N00014-07-1-0741
SubjectsFeature selection; Quadratic programming; Nyström method; Large data set; Highdimensional data; Informática
Rights© 2010 Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan and Carlos Santa Cruz
Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection method, named Quadratic Programming Feature Selection (QPFS), that reduces the task to a quadratic optimization problem. In order to limit the computational complexity of solving the optimization problem, QPFS uses the Nystr¨om method for approximate matrix diagonalization. QPFS is thus capable of dealing with very large data sets, for which the use of other methods is computationally expensive. In experiments with small and medium data sets, the QPFS method leads to classification accuracy similar to that of other successful techniques. For large data sets, QPFS is superior in terms of computational efficiency.
Google Scholar:Rodríguez-Luján, Irene - Elkan, Charles - Santa Cruz Fernández, Carlos - Huerta, Ramón
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