Uncorrelated Lasso

Sibao Chen, Chris H. Q. Ding, Bin Luo, Ying Xie

AAAI 2013 pp. 166-172

doi:10.1609/AAAI.V27I1.8576 /aaai/2013/chen2013aaai-uncorrelated/

Abstract

Lasso-type variable selection has increasingly expanded its machine learning applications. In this paper, uncorrelated Lasso is proposed for variable selection, where variable de-correlation is considered simultaneously with variable selection, so that selected variables are uncorrelated as much as possible. An effective iterative algorithm, with the proof of convergence, is presented to solve the sparse optimization problem. Experiments on benchmark data sets show that the proposed method has better classification performance than many state-of-the-art variable selection methods.

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Cite

Text

Chen et al. "Uncorrelated Lasso." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8576

Markdown

[Chen et al. "Uncorrelated Lasso." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/chen2013aaai-uncorrelated/) doi:10.1609/AAAI.V27I1.8576

BibTeX

@inproceedings{chen2013aaai-uncorrelated,
  title     = {{Uncorrelated Lasso}},
  author    = {Chen, Sibao and Ding, Chris H. Q. and Luo, Bin and Xie, Ying},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2013},
  pages     = {166-172},
  doi       = {10.1609/AAAI.V27I1.8576},
  url       = {https://mlanthology.org/aaai/2013/chen2013aaai-uncorrelated/}
}