Liao Huming, Chen Hongmei, Yin Tengyu, Yuan Zhong, Horng Shi-Jinn, Li Tianrui
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, 611756, China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, China.
College of Computer Science, Sichuan University, Chengdu 610065, China.
Neural Netw. 2025 Jan;181:106840. doi: 10.1016/j.neunet.2024.106840. Epub 2024 Oct 31.
Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required to obtain labels for data in the real world. Most of the existing embedded UFS utilize a sparse projection matrix for FS. However, this may introduce additional regularization terms, and it is difficult to control the sparsity of the projection matrix well. Moreover, such methods may seriously destroy the original feature structure in the embedding space. Instead, avoiding projecting the original data into the low-dimensional embedding space and identifying features directly from the raw features that perform well in the process of making the data show a distinct cluster structure is a feasible solution. Inspired by this, this paper proposes a model called A General Adaptive Unsupervised Feature Selection with Auto-weighting (GAWFS), which utilizes two techniques, non-negative matrix factorization, and adaptive graph learning, to simulate the process of dividing data into clusters, and identifies the features that are most discriminative in the clustering process by a feature weighting matrix Θ. Since the weighting matrix is sparse, it also plays the role of FS or a filter. Finally, experiments comparing GAWFS with several state-of-the-art UFS methods on synthetic datasets and real-world datasets are conducted, and the results demonstrate the superiority of the GAWFS.
特征选择（FS）在机器学习和数据挖掘中至关重要，因为它能使处理高维数据更高效、更可靠。由于在现实世界中获取数据标签需要额外资源，无监督特征选择（UFS）受到了更多关注。现有的大多数嵌入式UFS利用稀疏投影矩阵进行特征选择。然而，这可能会引入额外的正则化项，并且难以很好地控制投影矩阵的稀疏性。此外，此类方法可能会严重破坏嵌入空间中的原始特征结构。相反，避免将原始数据投影到低维嵌入空间，而是直接从在使数据呈现明显聚类结构的过程中表现良好的原始特征中识别特征，是一种可行的解决方案。受此启发，本文提出了一种名为带自动加权的通用自适应无监督特征选择（GAWFS）的模型，该模型利用非负矩阵分解和自适应图学习这两种技术来模拟将数据划分为聚类的过程，并通过特征加权矩阵Θ识别在聚类过程中最具判别力的特征。由于加权矩阵是稀疏的，它还起到了特征选择或过滤器的作用。最后，在合成数据集和真实世界数据集上进行了将GAWFS与几种先进的UFS方法进行比较的实验，结果证明了GAWFS的优越性。
