Quadratic Programming Feature Selection
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
M I T PressDate
2010-01-04Citation
Journal of Machine Learning Research 11 (2010): 1491-1516ISSN
1533-7928 (online); 1532-4435 (print)Funded by
I.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 N00014-07-1-0741Editor's Version
http://jmlr.org/papers/volume11/rodriguez-lujan10a/rodriguez-lujan10a.pdfSubjects
Feature selection; Quadratic programming; Nyström method; Large data set; Highdimensional data; InformáticaRights
© 2010 Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan and Carlos Santa CruzAbstract
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.
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Google Scholar:Rodríguez-Luján, Irene
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Elkan, Charles
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Santa Cruz Fernández, Carlos
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Huerta, Ramón
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