Proceedings:
Proceedings of the International Symposium on Combinatorial Search, 8
Volume
Issue:
Vol. 8 No. 1 (2015): Eighth Annual Symposium on Combinatorial Search
Track:
Short Papers
Downloads:
Abstract:
In this paper we treat the problem of feature selection in unsupervised learning as a state-space search problem. We introduce three different heuristic functions and perform extensive experiments on datasets with tens, hundreds, and thousands of features. Namely, we test different search algorithms using the heuristic functions we introduce. Our results show that the heuristic search approach for feature selection in unsupervised learning problems can be far superior than traditional baselines such as PCA and random projections.
DOI:
10.1609/socs.v6i1.18375
SOCS
Vol. 8 No. 1 (2015): Eighth Annual Symposium on Combinatorial Search