Feature Mining through Distance Minimization Learning

@inproceedings{Thomson2003FeatureMT,
  title={Feature Mining through Distance Minimization Learning},
  author={Jeffrey J. Thomson and Rex E. Gantenbein and Trevor Nielson},
  booktitle={IASSE},
  year={2003}
}
The problem of joint feature selection across a group of related tasks has applications in many areas including biomedical informatics and computer vision. We consider the 2,1-norm regularized regression model for joint feature selection from multiple tasks, which can be derived in the probabilistic framework by assuming a suitable prior from the exponential family. One appealing feature of the 2,1-norm regularization is that it encourages multiple predictors to share similar sparsity patterns… CONTINUE READING
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Referenced Papers

Publications referenced by this paper.
Showing 1-9 of 9 references

Introductory Lectures on Convex Optimization: A Basic Course

  • Y. Nesterov
  • Kluwer Academic Publishers,
  • 2003
Highly Influential
5 Excerpts

Convex Optimization & Euclidean Distance Geometry

  • J. Dattorro
  • Meboo Publishing,
  • 2005

Boosted lasso

  • P. Zhao, B. Yu
  • Technical report, Statistics Department, UC…
  • 2004
1 Excerpt

Convex Optimization

  • S. Boyd, L. Vandenberghe
  • 2004
1 Excerpt

Problem Complexity and Method Efficiency in Optimization

  • A. Nemirovski, D. Yudin
  • 1983
1 Excerpt

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