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
• DOI: 10.1007/s40747-024-01439-7
• ISSN: 2199-4536

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 mining. In this paper, we design a new semi-supervised multi-label feature selection algorithm. First, we construct an initial similarity matrix with supervised information by considering the similarity between labels, so as to learn a more ideal similarity matrix, which can better guide feature selection. By combining latent representation with semi-supervised information, a more ideal pseudo-label matrix is learned. Second, the local manifold structure of the original data space is preserved by the manifold regularization term based on the graph. Finally, an effective alternating iterative updating algorithm is applied to optimize the proposed model, and the experimental results on several datasets prove the effectiveness of the approach.