自适应图融合的缺失多视图聚类算法

doi:10.3778/j.issn.1002-8331.2201-0192

摘要/Abstract

摘要： 多视图聚类能充分利用不同视图间数据的一致性和差异性，引起越来越多的关注。传统多视图聚类方法假设每个视图的数据都是完整的，然而在实际应用中，收集到的多视图数据常存在部分视图缺失的样本。为了对缺失多视图数据进行聚类分析，提出自适应图融合的缺失多视图聚类算法（IMC_AGF）。算法以两两视图间共有样本为瞄点构建样本-样本的相似度矩阵，学习其一致性知识，再利用两两视图间的互补性，用自适应图融合算法整合所有的相似度图，获取缺失多视图数据完整的相似度矩阵，然后进行谱聚类得到分类结果。实验结果表明，提出的算法优于与之比较的经典缺失多视图聚类方法。

关键词: 缺失多视图聚类, 自适应图融合, 瞄点, 相似度矩阵, 聚类算法

Abstract: Multi-view clustering can make full use of the consistency and difference of samples between different views, which has attracted more and more attention. The traditional multi-view clustering method assumes that the samples of each view are complete, however, the collected multi-views data are usually incomplete in practical applications. In order to perform clustering analysis on incomplete multi-view data, an incomplete multi-view clustering algorithm with adaptive graph fusion（IMC_AGF） is proposed. In IMC_AGF, the shared samples between two views are used as the anchors to construct a sample-sample similarity matrix by learning its consistency knowledge. Then the complementarity between two views are exploited to integrate all the similarity maps with adaptive graph fusion method, and the final similarity matrix of incomplete multi-view data is obtain. Finally, the spectral clustering is used to get the clustering result. The experimental results show that the proposed algorithm is superior to the classical multi-view clustering method.

Key words: incomplete multi-view clustering, adaptive graph fusion, anchors, similarity matrix, clustering algorithm

黄展鹏, 吴杰康, 易法令. 自适应图融合的缺失多视图聚类算法[J]. 计算机工程与应用, 2023, 59(9): 176-181.

HUANG Zhanpeng, WU Jiekang, YI Faling. Incomplete Multi-View Clustering Algorithm with Adaptive Graph Fusion[J]. Computer Engineering and Applications, 2023, 59(9): 176-181.

参考文献

[1] 杨秀璋，武帅，杨琪，等.多视图融合TextRCNN 的论文自动推荐算法[J].计算机工程与应用，2023，59（2）：110-119.
YANG X Z，WU S，YANG Q，et al.Automatic paper recommendation algorithm based on multi-view fusion Text-RCNN[J].Computer Engineering and Applications，2023，59（2）：110-119.
[2] KUMAR-SINHA C，CHINNAIYAN A M.Precision oncology in the age of integrative genomics[J].Nature Biotechnology，2018，36（1）：46-60.
[3] JING Z，XIE X J，XU X，et al.Multi-view learning overview：recent progress and new challenges[J].Information Fusion，2017，3843-54.
[4] 徐霜，余琍.利用正则化矩阵分解技术的多视图聚类方法[J].计算机工程与应用，2019，55（14）：142-147.
XU S，YU L.Regularized matrix factorization algorithm for multi-view data clustering[J].Computer Engineering and Applications，2019，55（14）：142-147.
[5] WANG X M，ZHANG T Z，GAO X B.Multiview clustering based on non-negative matrix factorization and pairwise measurements[J].IEEE Transactions on Cybernetics，2019，49（9）：3333-3346.
[6] BO W，MEZLINI A M，DEMIR F，et al.Similarity network fusion for aggregating data types on a genomic scale[J].Nature Methods，2014，11（3）：333-337.
[7] ZHU X，ZHANG S，HE W，et al.One-step multiview spectral clustering[J].IEEE Transactions on Knowledge and Data Engineering，2019，31（10）：2022-2034.
[8] KIRK P，GRIFFIN J E，SAVAGE R S，et al.Bayesian correlated clustering to integrate multiple datasets[J].Bioinformatics，2012，28（24）：3290-3297.
[9] MO Q，SHEN R，GUO C，et al.A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data[J].Biostatistics，2018，19（1）：71-86.
[10] WEN J，FANG X Z，XU Y，et al.Low-rank representation with adaptive graph regularization[J].Neural Networks，2018，108：83-96.
[11] 朱丹，陈晓红，吴卿源，等.自适应图学习诱导的子空间聚类[J].计算机工程与应用，2020，56（21）：30-37.
ZHU D，CHEN X H，WU Q Y，et al.Subspace clustering induced by adaptive graph learning[J].Computer Engineering and Applications，2020，56（21）：30-37.
[12] SHI Q，HU B，ZENG T，et al.Multiview subspace clustering analysis for aggregating multiple heterogeneous omics data[J].Frontiers in Genetics，2019，10：744-754.
[13] LIU X，ZHU X，LI M.Multiple kernel k-means with incomplete kernels[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2020，42（5）：1191-1204.
[14] ZHANG P，WANG S W，HU J T，et al.Adaptive weighted graph fusion incomplete multi-view subspace clustering[J].Sensors，2020，20（20）：5755.
[15] WEN J，YAN K，ZHANG Z，et al.Adaptive graph completion based incomplete multi-view clustering[J].IEEE Transactions on Multimedia，2021，23：2493-2504.
[16] TRIVEDI A，RAI P，DAUMé H，et al.Multiview clustering with incomplete views[C]//Proceedings of NIPS Workshop，2010：1-7.
[17] LI S Y，JIANG Y，ZHOU Z H.Partial multi-view clustering[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence，Québec，QC，Canada，27-31 July，2014：1968-1974.
[18] ZHAO H，LIU H，FU Y.Incomplete multi-modal visual data grouping[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence，New York，NY，USA，9-15 July，2016：2392-2398.
[19] SHAO W，HE L，YU P S.Multiple incomplete views clustering via weighted nonnegative matrix factorization with [l2,1] regularization[C]//Proceedings of the 2015 European Conference on Machine Learning and Knowledge Discovery in Databases，Porto，Portugal，7-11 September 2015.Berlin，Heidelberg，Germany：Springer，2015：318-334.
[20] HU M，CHEN S.Doubly aligned incomplete multi-view clustering[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence，Stockholm，Sweden，13-19 July，2018：2262-2268.
[21] 赵博宇，张长青，陈蕾，等.生成式不完整多视图数据聚类[J].自动化学报，2021，47（8）：1867-1875.
ZHAO B Y，ZHANG C Q，CHEN L，et al.Generative model for partial multi-view clustering[J].Acta Automatica Sinica，2021，47（8）：1867-1875.
[22] GUO J，YE J.Anchors bring ease：an embarrassingly simple approach to partial multi-view clustering[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019：118-125.
[23] SA V R D，GALLAGHER P W，LEWIS J M，et al.Multi-view kernel construction[J].Machine Learning，2010，79（1）：47-71.
[24] MALLAH C，COPE J，ORWELL J.Plant leaf classification using probabilistic integration of shape，texture and margin features[C]//IASTED International Conference on Signal Processing，Pattern Recognition and Applications，2013：45-54.