Unsupervised Feature Selection via Adaptive Graph Learning and Constraint

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 3; pp. 1355 - 1362
• Main Authors: Zhang, Rui, Zhang, Yunxing, Li, Xuelong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive graph learning; Adaptive learning; Algorithms; Closed-form solutions; Constraints; Data points; Feature extraction; Feature selection; graph constraint; Laplace equations; Learning; Learning systems; Machine learning; Manifolds; Optimization; Similarity; sparsity; Symmetric matrices; unsupervised feature selection

The performance of graph-based feature selection methods relies heavily on the quality of the construction of the similarity matrix. However, most of the graphs on these methods are initially fixed, where few of them are constrained. Once the graph is determined, it will remain constant in the whole...