Unsupervised feature selection via graph matrix learning and the low-dimensional space learning for classification. (January 2020)

• Record Type: Journal Article
• Authors: Han, Xiaohong; Liu, Ping; Wang, Li; Li, Dengao
• Is Part Of: Engineering applications of artificial intelligence. Volume 87(2020)
• Publication Date: 2020-01
• Subjects: Feature selection -- Graph matrix -- Dimensionality reduction -- Manifold learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.103283
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Unsupervised feature selection is a powerful tool to select a subset of features for effective representation of high-dimensional data. In this paper, we proposes a novel unsupervised feature selection method via the graph matrix learning and the low-dimensional space learning to obtain their individually optimized result. Furthermore, the global and local correlation of features have been taken into consideration through the low-rank constraint and the feature-level representation property on the graph matrix. Experimental analysis on 15 benchmark datasets verified that our proposed method outperformed the state-of-the-art feature selection methods in terms of classification performance.