Non-Convex Feature Learning via ` p , ∞ Operator

@inproceedings{Kong2014NonConvexFL,
  title={Non-Convex Feature Learning via ` p , ∞ Operator},
  author={Deguang Kong and Chris Ding},
  year={2014}
}
We present a feature selection method for solving sparse regularization problem, which has a composite regularization of `p norm and `∞ norm. We use proximal gradient method to solve this `p,∞ operator problem, where a simple but efficient algorithm is designed to minimize a relatively simple objective function, which contains a vector of `2 norm and `∞ norm. Proposed method brings some insight for solving sparsity-favoring norm, and extensive experiments are conducted to characterize the… CONTINUE READING
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Feature Selection for Knowledge Discovery and Data Mining

  • H. Liu, H. Motoda
  • Springer.
  • 1998
Highly Influential
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