Unsupervised Feature Selection via Neural Networks and Self-Expression with Adaptive Graph Constraint. (March 2023)

• Record Type: Journal Article
• Authors: You, Mengbo; Yuan, Aihong; He, Dongjian; Li, Xuelong
• Is Part Of: Pattern recognition. Volume 135(2023)
• Publication Date: 2023-03
• Subjects: Unsupervised feature selection -- Manifold structure -- Adaptive graph constraint -- Neural networks; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109173
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper proposes a method to replace the linear mapping-based spectral analysis with neural networks, which is more appropriate to learn the nonlinear mapping relationship between the original data and the pseudo cluster label. Through constructing two different adaptive graph constraints, the mutual interaction between the NNs-based pseudo label learning module and the self-expression module is facilitated to explore the feature selection priority for the raw data. Through a lot of well-designed experiments, the effectiveness of the method proposed in this paper has been fully proven. Abstract: Unsupervised feature selection (UFS), which selects the most important feature subset and eliminates the unnecessary information for the upcoming data analysis, is a significant problem in machine learning and has been explored for years. Most UFS methods map features into a pseudo label space by multiplying a projection matrix constrained with sparsity to learn the mapping from the features to the labels. However, the mapping relationship is usually not linear, and linear regression may result in a suboptimal selection. To address this issue, we propose a novel UFS method, called neural networks embedded self-expression (NNSE). NNSE replaces the linear regression of traditional spectral analysis methods with neural networks to learn the pseudo label space. Besides, we embed neural networks into the self-expression model to improve the representative ability by preserving the local structure with an adaptive graph regularization module. Then we propose an efficient alternative iterative algorithm to solve the proposed model. Experimental results on 8 public datasets show NNSE outperforms the other state-of-the-art methods. Moreover, experimental results are also presented to show the convergence of the proposed method. The source code is available at: https://github.com/misteru/NNSE .