Feature Selection

Feature Selection with Annealing: A generic method for feature selection and model learning that outperforms boosting and methods based on sparsity inducing penalties such as L1, SCAD, and MCP.

Publications:
  • M. Wang, A. Barbu. Are screening methods useful in feature selection? An empirical study. To appear in PLoS One. (arxiv)
  • A. Barbu, Y. She, L. Ding, G. Gramajo. Feature Selection with Annealing for Computer Vision and Big Data Learning. IEEE PAMI 39, No. 2, 272–286, 2017. (arxivlink, Matlab code, Github)
  • F. Bunea, A. Tsybakov, M. Wegkamp and A.Barbu. SPADES and mixture models. Annals of Statistics 38, No. 4, 2525–2558, 2010. (pdf, code)
  • F. Bunea and A.Barbu. Dimension reduction and variable selection in case control studies via regularized likelihood optimization. Electronic Journal of Statistics, 3, 2009. (pdf)

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