计算机科学 ›› 2025, Vol. 52 ›› Issue (7): 69-74.doi: 10.11896/jsjkx.240500097
王劲夫1, 王思为2, 梁伟轩1, 于胜举1, 祝恩1
WANG Jinfu1, WANG Siwei2, LIANG Weixuan1, YU Shengju1, ZHU En1
摘要: 多视图聚类是无监督学习领域的一个研究热点。最近,基于跨视图图扩散的方法有效利用了多个视图之间的互补信息,取得了较好的效果。但这类方法的时间和空间复杂度较高,限制了其在大规模数据集上的应用。针对此问题,提出基于二部图跨视图图扩散的多视图聚类方法,成功将立方的时间复杂度和平方的空间复杂度降低至线性,从而可以高效地处理大规模聚类任务。使用二部图代替全图进行跨视图图扩散,并对基于全图的跨视图图扩散公式进行修改以适应二部图输入。在6个基准数据集上的实验结果表明,所提出的方法在聚类精度和运行效率方面比大多现有多视图聚类方法更具优势。在小规模数据集上,所提方法中的准确度等指标普遍高于对比算法5%以上;在大规模数据集上,所提方法的优势更加明显,其ACC和NMI等指标高于对比算法15%~30%。
中图分类号:
[1]WANG H,YANG Y.Multi-view clustering:A survey[J].Big Data Mining and Analytics,2018,1(2):83-107. [2]ZHAO M,YANG W,NIE F.Auto-weighted orthogonal andnonnegative graph reconstruction for multi-view clustering[J].Information Sciences,2023,632:324-339. [3]DONG Y,CHE H,LEUNG M,et al.Centric graph regularized log-norm sparse non-negative matrix factorization for multi-view clustering[J].Signal Processing,2024,217:109341. [4]DU Y,LU G F,JI G,et al.Robust and optimal neighborhood graph learning for multi-view clustering[J].Information Sciences,2023,631:429-448 [5]XIN Y J,CAI J H,HE Y T,et al.Multi-view Clustering Based on Cross-structural Feature Selection and Graph Cycle AdaptiveLearning[J]. Computer Science,2025,52(2):145-157. [6]TENG S H,SHENG W T,TENG L Y.Multiview Graph Clustering with Fusion of Weighted Inconsistency[J].Journal of Chinese Computer Systems,2025,46(2):381-388. [7]TANG C,LIU X,ZHU X,et al.CGD:Multi-view clustering via cross-view graph diffusion[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:5924-5931. [8]LI Q,AN S,LI L,et al.Multi-view diffusion process for spectral clustering and image retrieval[J].IEEE Transactions on Image Processing,2023,32:4610-4620. [9]WEN J,ZHANG Z,ZHANG Z,et al.Generalized incompletemultiview clustering with flexible locality structure diffusion[J].IEEE Transactions on Cybernetics,2021,51(1):101-114. [10]ZHOU D,WESTON J,GRETTON A,et al.Ranking on data mani-folds[C]//Proceedings of the 17th International Conference on Neural Information Processing Systems.2003:169-176. [11]SONG B,XIANG B,QI T,et al.Regularized diffusion process for visual retrieval[C]//Proceedings of the Thirty-first AAAI Conference on Artificial Intelligence.2017:3967-3973. [12]LIU W,HE J,CHANG S.Large graph construction for scalable semi-supervised learning[C]//Proceedings of the 27th International Conference on Machine Learning.Haifa,Israel,2010. [13]DONOSER M,BISCHOF H.Diffusion processes for retrievalrevisited[C]//Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition.2013:1320-1327. [14]XIAO C,NIE F,HUANG H.Multi-view k-means clustering on big data[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence.AAAI Press,2013:2598-2604. [15]TANG C,LI Z,WANG J,et al.Unified one-step multi-viewspectral clustering[J].IEEE Transactions on Knowledge and Data Engineering,2022,35(6):6449-6460. [16]LI R,ZHANG C,HU Q,et al.Flexible multi-view representation learning for subspace clustering[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.2019:2916-2922. [17]ZHAO K,ZHAO X,CHONG P,et al.Partition level multi-view sub-space clustering[J].Neural Networks,2020,122:279-288. [18]KANG Z,ZHOU W,ZHAO Z,et al.Large-scale multi-view sub-space clustering in linear time[C]//National Conference on Artificial Intelligence.Association for the Advancement of Artificial Intelligence,2020:4412-4419. [19]SUN M,ZHANG P,WANG S,et al.Scalable multi-view subspace clustering with unified anchors[C]//Proceedings of the 29th ACM International Conference on Multimedia.Virtual Event China,2021:ACM:3528-3536. [20]TAN Y,LIU Y,HUANG S,et al.Sample-level multi-viewgraph clustering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2023,23966-23975. [21]LI Q,AN S,LI L,et al.Multi-view diffusion process for spectral clustering and image retrieval[J].IEEE Transactions on Image Processing,2023.32:4610-4620. [22]YU S,WANG S,DONG Z,et al.A non-parametric graph clustering framework form multi-view data[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2024:16558-16567. |
|