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Quadratic programming feature selection. (English) Zbl 1242.68245
Summary: 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 Nyström 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.

MSC:
68T05 Learning and adaptive systems in artificial intelligence
90C20 Quadratic programming
62H30 Classification and discrimination; cluster analysis (statistical aspects)
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