Ensemble Method for Unsupervised Feature Selection
Yihui Luo, Shuchu Xiong · 2009
For many large-scale datasets it is necessary to reduce dimensionality to the point where further exploration and analysis can take place. As a result, it is important to develop techniques for selecting features from large-scale datasets. However this topic has been well studied in supervised learning area, there are only a few methods proposed for feature selection for clustering. In this paper, we propose a novel ensemble unsupervised feature selection algorithm, in which individual component algorithm uses cluster result obtained in the space of a feature subset of original features to only evaluate every feature in that feature subset. Our experiments with several data sets demonstrate that the proposed algorithm is able to obtain a better and more stable feature subset compared with other existing unsupervised feature selection algorithms.