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

Hiromasa Arai, Ke Xu, Crystal Maung, Haim Schweitzer

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.