Robust unsupervised feature selection via data relationship learning

Unsupervised feature selection robust to many outliers is a challenging task. The crucial difficulty is learn-ing a robust subspace, which preserves local structure. The most common solution is to reduce fitting error by applying different robust norms. However, there are three shortcomings. Firstly, they are not ro-bust enough when outliers distributed both randomly and concentratedly are widely present. Secondly, outlier removal is not considered. Thirdly, it is not easy to understand and choose an euclidean distance threshold that decides a sample as an outlier in different scenarios. The first two shortcomings make previous methods fail to achieve their expected learning results, and the third one increases the appli-cation difficulty in different fields. To address these issues, a robust unsupervised feature selection via data relationship learning (RUFSDR) is proposed in this paper. Specifically, scores representing the data's importance will be learned and assigned to each sample. Inliers will be given different positive scores. Outliers will be given 0 such that a subspace, which preserves the local structure better, can be learned without prior knowledge about the distance threshold. The experiments conducted on various datasets with several scenarios show the superiority of RUFSDR.(c) 2023 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a robust unsupervised feature selection method that can effectively deal with the influence of many outliers on model performance. By learning a robust subspace that preserves local structure and addressing the shortcomings of traditional methods through outlier removal and Euclidean distance threshold setting, the superiority of the proposed method is demonstrated through experiments.