东北大学学报(自然科学版) ›› 2024, Vol. 45 ›› Issue (12): 1706-1716.DOI: 10.12068/j.issn.1005-3026.2024.12.005

• 信息与控制 • 上一篇    

基于稀疏自表示及流形正则化的无监督特征选择

刘杰, 谭文静, 李占山()   

  1. 吉林大学 计算机科学与技术学院,吉林 长春 130012
  • 收稿日期:2023-06-09 出版日期:2024-12-10 发布日期:2025-03-18
  • 通讯作者: 李占山
  • 作者简介:刘 杰(1973-),女,吉林长春人,吉林大学副教授
    李占山(1966-),男,吉林长春人,吉林大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(62276060);吉林省发展和改革委员会资助项目(2019C053-9)

Unsupervised Feature Selection Based on Sparse Self-representation with Manifold Regularization

Jie LIU, Wen-jing TAN, Zhan-shan LI()   

  1. College of Computer Science and Technology,Jilin University,Changchun 130012,China.
  • Received:2023-06-09 Online:2024-12-10 Published:2025-03-18
  • Contact: Zhan-shan LI

摘要:

基于自表示的无监督特征选择能够处理未标记数据且不受伪标签影响.为了令此类方法同时具有良好的鲁棒性、保留样本局部结构、能选出最具代表性的特征,提出了一种新的方法,并设计了一个对应的迭代优化算法来计算其目标函数.该方法先对样本异常值进行识别和处理,然后将传统的自表示模型与非凸稀疏约束和流形正则结合形成目标模型,再将预处理后的数据放入模型进行特征选择,最后使用所选特征进行聚类.将所提方法在9个真实数据集上与7种方法进行对比实验,实验结果表明,所提方法可以有效解决无监督特征选择问题.

关键词: 无监督特征选择, 自表示, 鲁棒, 稀疏, 流形正则化

Abstract:

Self‑representation based unsupervised feature selection can handle unlabeled data without being affected by pseudo‑labeling. To ensure that such methods simultaneously achieve good robustness, preserve the local structure of samples, and select the most representative features, a new approach is proposed, and a corresponding iterative optimization algorithm is designed to compute its objective function. The method first identifies and processes outliers of samples, then combines the traditional self‑representation model with non‑convex sparse constraint and manifold regularization to form the target model, and puts the preprocessed data into the model for feature selection. Finally, the method uses the selected features for clustering. The proposed method is compared with seven methods on nine real data sets for experiments, and the experimental results show that the proposed method can effectively solve the unsupervised feature selection problem.

Key words: unsupervised feature selection, self?representation, robust, sparse, manifold regularization

中图分类号: