Convex Principal Feature Selection

@inproceedings{Masaeli2010ConvexPF,
  title={Convex Principal Feature Selection},
  author={Mahdokht Masaeli and Yan Yan and Ying Cui and Glenn Fung and Jennifer G. Dy},
  booktitle={SDM},
  year={2010}
}
A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the original features are important. There is a recent interest in sparse PCA (SPCA). By applying an L1 regularizer to PCA, a sparse transformation is achieved. However, true feature selection may not be achieved as non-sparse coefficients may be distributed over several features. Feature selection is an NP-hard… CONTINUE READING
Highly Cited
This paper has 30 citations. REVIEW CITATIONS

References

Publications referenced by this paper.
Showing 1-10 of 20 references

Convex Optimization

  • Stephen Boyd, Lieven Vandenberghe
  • 2004
1 Excerpt

Aha . Uci repository of machine learning databases

  • P. Murphy, D.
  • 2003

An introduction to variable and feature selection

  • Isabelle Guyon, André, Elisseeff
  • Journal of Machine Learning Research,
  • 2003
1 Excerpt

Similar Papers

Loading similar papers…