基于跨结构特征选择和图循环自适应学习的多视图聚类  

Multi-view Clustering Based on Cross-structural Feature Selection and Graph Cycle Adaptive Learning

作　　者：辛永杰 蔡江辉[1,3] 贺艳婷 苏美红 史晨辉 杨海峰 XIN Yongjie;CAI Jianghui;HE Yanting;SU Meihong;SHI Chenhui;YANG Haifeng(School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China;Shanxi Key Laboratory of Big Data Analysis and Parallel Computing,Taiyuan 030024,China;School of Computer Science and Technology,North University of China,Taiyuan 030051,China)

机构地区：[1]太原科技大学计算机科学与技术学院,太原030024 [2]山西省大数据分析和并行计算重点实验室,太原030024 [3]中北大学计算机科学与技术学院,太原030051

出　　处：《计算机科学》2025年第2期145-157,共13页Computer Science

基　　金：国家自然科学基金(U1931209);山西省科技合作与交流项目(202204041101037,202204041101033);太原科技大学研究生教育创新项目(BY2023015)。

摘　　要：现有的大多数图自适应学习方法依赖于高维原始数据,且数据中不可避免地会出现噪声或信息缺失等现象,导致无法精准地选择出高维数据中的重要特征信息。此外,还忽视了在特征选择过程中多视图表示结构上的关联性。针对以上问题,提出了一种基于跨结构特征选择和图循环自适应学习的多视图聚类方法(MLFS-GCA)。首先,设计了一个跨结构特征选择框架。通过联合学习多视图表示的空间结构特点和聚类结构的一致性,将高维数据投影到低维线性子空间中,并在视图特定的基矩阵和一致性聚类结构的辅助下学习低维特征表示。其次,提出图循环自适应学习模块。通过k最邻近法(k-NN)选取投影空间中k个最近邻点,并协同矩阵低秩学习来循环地优化相似结构。最后,学习得到用于聚类任务的共享稀疏相似矩阵。通过在各种真实的多视图数据集上进行大量实验,验证了在多视图聚类中图循环自适应学习的优越性。Most of the existing graph adaptive learning methods rely on high-dimensional raw data and inevitably have phenomena such as noise or missing information in the data,resulting in the inability to accurately select the important feature information in the high-dimensional data,in addition to ignoring the structural relevance of the multi-view representations in the feature selection process.To tackle the above problems,a multi-view clustering method(MLFS-GCA)based on cross-structural feature selection and graph cycle adaptive learning is proposed.First,a cross-structural feature selection framework is designed.By jointly learning the spatial structure characteristics of multi-view representations and the consistency of the clustering structure,the high-dimensional data is projected into a low-dimensional linear subspace,and the low-dimensional feature representation is learned with the assistance of view-specific basis matrix and consistent clustering structures.Second,a graph cycle adaptive learning module is proposed.The k nearest neighbors in the projection space are selected by the k-nearest neighbor(k-NN)method,and the similar structures are optimized cyclically in concert with matrix low-rank learning.Eventually,the shared sparse similarity matrix for clustering task is learned.The superiority of graph cycle adaptive learning in multi-view clustering is demonstrated through extensive experiments on various real multi-view datasets.

关 键 词：多视图聚类 图循环自适应学习 跨结构特征选择 K-NN 矩阵低秩学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]