Low-rank adaptive graph embedding for unsupervised feature extraction
作者：
Highlights：
• We propose a novel unsupervised feature extraction method named LRAGE, which can learn informative projection and a meaningful adaptive graph with probabilistic weight jointly based on reconstruction error minimization.
• We impose a low- rank constraint on the model to learn more informative projection. The potential properties of LRAGE is well revealed by comparing it with several similar models on both synthetic and real-world data sets.
• An iterative algorithm is elaborately designed to solve the resulting optimization problem. The convergence of the proposed algorithm is proved and the corresponding computational complexity analysis is also presented. Extensive experiments indicate the promising performance of the proposed LRAGE, especially on the corrupted data sets.
• Extensive experiments indicate the promising performance of the proposed LRAGE, especially on the corrupted data sets.
摘要
•We propose a novel unsupervised feature extraction method named LRAGE, which can learn informative projection and a meaningful adaptive graph with probabilistic weight jointly based on reconstruction error minimization.•We impose a low- rank constraint on the model to learn more informative projection. The potential properties of LRAGE is well revealed by comparing it with several similar models on both synthetic and real-world data sets.•An iterative algorithm is elaborately designed to solve the resulting optimization problem. The convergence of the proposed algorithm is proved and the corresponding computational complexity analysis is also presented. Extensive experiments indicate the promising performance of the proposed LRAGE, especially on the corrupted data sets.•Extensive experiments indicate the promising performance of the proposed LRAGE, especially on the corrupted data sets.
论文关键词：Low-rank regression,Jointly sparse learning,Adaptive graph embedding,Unsupervised feature extraction
论文评审过程：Received 2 September 2019, Revised 28 July 2020, Accepted 13 November 2020, Available online 20 November 2020, Version of Record 19 February 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107758
