Weighted A* Algorithms for Unsupervised Feature Selection with Provable Bounds on Suboptimality

Hiromasa Arai, Ke Xu, Crystal Maung, Haim Schweitzer

AAAI 2016 pp. 4194-4195

doi:10.1609/AAAI.V30I1.9950 /aaai/2016/arai2016aaai-weighted/

Abstract

Identifying a small number of features that can represent the data is believed to be NP-hard. Previous approaches exploit algebraic structure and use randomization. We propose an algorithm based on ideas similar to the Weighted A* algorithm in heuristic search. Our experiments show this new algorithm to be more accurate than the current state of the art.

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Text

Arai et al. "Weighted A* Algorithms for Unsupervised Feature Selection with Provable Bounds on Suboptimality." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9950

Markdown

[Arai et al. "Weighted A* Algorithms for Unsupervised Feature Selection with Provable Bounds on Suboptimality." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/arai2016aaai-weighted/) doi:10.1609/AAAI.V30I1.9950

BibTeX

@inproceedings{arai2016aaai-weighted,
  title     = {{Weighted A* Algorithms for Unsupervised Feature Selection with Provable Bounds on Suboptimality}},
  author    = {Arai, Hiromasa and Xu, Ke and Maung, Crystal and Schweitzer, Haim},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {4194-4195},
  doi       = {10.1609/AAAI.V30I1.9950},
  url       = {https://mlanthology.org/aaai/2016/arai2016aaai-weighted/}
}