Sparse semi-supervised multi-label feature selection based on latent representation

• Published in: Complex & intelligent systems Vol. 10; no. 4; pp. 5139 - 5151
• Main Authors: Zhao, Xue, Li, Qiaoyan, Xing, Zhiwei, Yang, Xiaofei, Dai, Xuezhen
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2024; Springer Nature B.V; Springer
• Subjects: Algorithms; Complexity; Computational Intelligence; Data mining; Data Structures and Information Theory; Effectiveness; Engineering; Feature selection; Labels; Latent representation; Machine learning; Manifold regularization; Manifolds (mathematics); Multi-label learning; Original Article; Regularization; Representations; Similarity; Similarity matrix; Latent representation; Multi-label learning; Feature selection; Similarity matrix; Manifold regularization

With the rapid development of the Internet, there are a large number of high-dimensional multi-label data to be processed in real life. To save resources and time, semi-supervised multi-label feature selection, as a dimension reduction method, has been widely used in many machine learning and data m...