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

Authors

  • Hiromasa Arai The University of Texas at Dallas
  • Ke Xu The University of Texas at Dallas
  • Crystal Maung The University of Texas at Dallas
  • Haim Schweitzer The University of Texas at Dallas

DOI:

https://doi.org/10.1609/aaai.v30i1.9950

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.

AAAI 2016 Proceedings Cover

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Published

2016-03-05

How to Cite

Arai, H., Xu, K., Maung, C., & Schweitzer, H. (2016). Weighted A* Algorithms for Unsupervised Feature Selection with Provable Bounds on Suboptimality. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9950

Issue

Vol. 30 No. 1 (2016): Thirtieth AAAI Conference on Artificial Intelligence

Section

Student Abstracts and Posters