Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach

@article{Roffo2017InfiniteLF,
  title={Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach},
  author={Giorgio Roffo and Simone Melzi and Umberto Castellani and Alessandro Vinciarelli},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={1407-1415}
}
Feature selection is playing an increasingly significant role with respect to many computer vision applications spanning from object recognition to visual object tracking. However, most of the recent solutions in feature selection are not robust across different and heterogeneous set of data. In this paper, we address this issue proposing a robust probabilistic latent graph-based feature selection algorithm that performs the ranking step while considering all the possible subsets of features… 

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