A new unsupervised feature selection algorithm using similarity-based feature clustering

Abstract

Unsupervised feature selection is an important problem, especially for high-dimensional data. However, until now, it has been scarcely studied and the existing algorithms cannot provide satisfying performance. Thus, in this paper, we propose a new unsupervised feature selection algorithm using similarity-based feature clustering, Feature Selection-based Feature Clustering (FSFC). FSFC removes redundant features according to the results of feature clustering based on feature similarity. First, it clusters the features according to their similarity. A new feature clustering algorithm is proposed, which overcomes the shortcomings of K-means. Second, it selects a representative feature from each cluster, which contains most interesting information of features in the cluster. The efficiency and effectiveness of FSFC are tested upon real-world data sets and compared with two representative unsupervised feature selection algorithms, Feature Selection Using Similarity (FSUS) and Multi-Cluster-based Feature Selection (MCFS) in terms of runtime, feature compression ratio, and the clustering results of K-means. The results show that FSFC can not only reduce the feature space in less time, but also significantly improve the clustering performance of K-means.