Quadratic Programming Feature Selection

  title={Quadratic Programming Feature Selection},
  author={Irene Rodr{\'i}guez-Luj{\'a}n and Ram{\'o}n Huerta and Charles Elkan and Carlos Santa Cruz},
  journal={Journal of Machine Learning Research},
Identifying a subset of features that preserves classificat ion ccuracy is a problem of growing importance, because of the increasing size and dimensionalit y 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 öm method for approximate matrix diagonalization. QPFS… CONTINUE READING
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