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

被引：0

作者：

Arai, Hiromasa ^{[1
]}

Xu, Ke ^{[1
]}

Maung, Crystal ^{[1
]}

Schweitzer, Haim ^{[1
]}

机构：

[1] Univ Texas Dallas, Dept Comp Sci, 800 W Campbell Rd, Richardson, TX 75080 USA

来源：

THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2016年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：4194 / 4195

页数：2

共 8 条

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