Multi-Cluster Feature Selection Based on Isometric Mapping

Abstract

This letter presents an unsupervised feature selection method based on machine learning. Feature selection is an important component of artificial intelligence, machine learning, which can effectively solve the curse of dimensionality problem. Since most of the labeled data is expensive to obtain, this paper focuses on the unsupervised feature selection method. The distance metric of traditional unsupervised feature selection algorithms is usually based on Euclidean distance, and it is maybe unreasonable to map high-dimensional data into low-dimensional space by using Euclidean distance. Inspired by this, this paper combines manifold learning to improve the multi-cluster unsupervised feature selection algorithm. By using geodesic distance, we propose a multi-cluster feature selection based on isometric mapping (MCFS-I) algorithm to perform unsupervised feature selection adaptively for multiple clusters. Experimental results show that the proposed method consistently improves the clustering performance compared to the existing competing methods.