作者: Julian R. H. Mariño , Levi H. S. Lelis
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摘要: 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, thousands features. Namely, test algorithms using introduce. Our results show that approach for problems can be far superior than traditional baselines such PCA random projections.