SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

• Published in: Information (Basel) Vol. 15; no. 1; p. 57
• Main Authors: Yi, Yugen, Zhang, Haoming, Zhang, Ningyi, Zhou, Wei, Huang, Xiaomei, Xie, Gengsheng, Zheng, Caixia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: adaptive graph learning; Classification; Clustering; Computational linguistics; Construction; Datasets; Discriminant analysis; Efficiency; Feature selection; Graphs; label propagation; Language processing; Learning; Methods; Natural language interfaces; semi-supervised learning; sparse regularization; Iran

As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature selection methods (SSFS) have been proposed to s...