{"type":"ScholarlyArticle","title":"Multi-View Multiple Clustering","authors":[{"name":"Shixin Yao","slug":"shixin-yao","given":"Shixin","family":"Yao","affiliation":"College of Computer and Information Sciences , Southwest University , Chongqing , China"},{"name":"Guoxian Yu","slug":"guoxian-yu","given":"Guoxian","family":"Yu","affiliation":"College of Computer and Information Sciences , Southwest University , Chongqing , China; CEMSE , King Abdullah University of Science and Technology , Thuwal , SA"},{"name":"Jun Wang","slug":"jun-wang","given":"Jun","family":"Wang","affiliation":"College of Computer and Information Sciences , Southwest University , Chongqing , China"},{"name":"Carlotta Domeniconi","slug":"carlotta-domeniconi","given":"Carlotta","family":"Domeniconi","affiliation":"Department of Computer Science , George Mason University , VA , USA"},{"name":"Xiangliang Zhang","slug":"xiangliang-zhang","given":"Xiangliang","family":"Zhang","affiliation":"CEMSE , King Abdullah University of Science and Technology , Thuwal , SA"}],"abstract":"Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are designed for single-view data. We assume that the individuality and commonality of multi-view data can be leveraged to generate high-quality and diverse clusterings. To this end, we propose a novel multi-view multiple clustering (MVMC) algorithm. MVMC first adapts multi-view self-representation learning to explore the individuality encoding matrices and the shared commonality matrix of multi-view data. It additionally reduces the redundancy (i.e., enhancing the individuality) among the matrices using the Hilbert-Schmidt Independence Criterion (HSIC), and collects shared information by forcing the shared matrix to be smooth across all views. It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality. We further extend multiple co-clustering on multi-view data and propose a solution called multi-view multiple co-clustering (MVMCC). Our empirical study shows that MVMC (MVMCC) can exploit multi-view data to generate multiple high-quality and diverse clusterings (co-clusterings), with superior performance to the state-of-the-art methods.","doi":"10.24963/ijcai.2019/572","url":"https://ijcai.org/proceedings/2019/572","pdf":"https://ijcai.org/proceedings/2019/0572.pdf","markdown":"https://ijcai.org/proceedings/2019/572.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/572","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":572,"pages":{"first":4121,"last":4127},"keywords":["Machine Learning: Clustering","Machine Learning: Data Mining","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Bae and Bailey, 2006] Eric Bae and James Bailey. Coala: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. In ICDM, pages 53-62, 2006.","title":"Coala: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity","authors":"Bailey ; Bae, Eric Bae, James Bailey","year":"2006","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bailey, 2013] James Bailey. Alternative clustering analysis: A review. In Aggarwal Charu and Reddy Chandan, edi- tors, Data Clustering: Algorithms and Applications, pages 535-550. CRC Press, 2013.","title":"Alternative clustering analysis: A review","authors":"James Bailey","year":"2013","venue":"editors, Data Clustering: Algorithms and Applications","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Belkin et al., 2006] Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled exam- ples. JMLR, 7(11):2399-2434, 2006.","title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","authors":"Mikhail Belkin, Partha Niyogi, Vikas Sindhwani","year":"2006","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2015] Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, and Hua Zhang. Diversity-induced multi-view subspace clustering. In CVPR, pages 586-594, 2015.","title":"Diversity-induced multi-view subspace clustering","authors":"Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Caruana et al., 2006] Rich Caruana, Mohamed Elhawary, Nam Nguyen, and Casey Smith. Meta clustering. In ICD- M, pages 107-118, 2006.","title":"Meta clustering","authors":"Rich Caruana, Mohamed Elhawary, Nam Nguyen, Casey Smith","year":"2006","venue":"ICD-M","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chao et al., 2017] Guoqing Chao, Shiliang Sun, and Jinbo Bi. A survey on multi-view clustering. arXiv preprint arXiv:1712.06246, 2017.","title":"A survey on multi-view clustering","authors":"Guoqing Chao, Shiliang Sun, Jinbo Bi","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cui et al., 2007] Ying Cui, Xiaoli Z Fern, and Jennifer G Dy. Non-redundant multi-view clustering via orthogonal- ization. In ICDM, pages 133-142, 2007.","title":"Non-redundant multi-view clustering via orthogonalization","authors":"Ying Cui, Jennifer G Xiaoli Z Fern, Dy","year":"2007","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dang and Bailey, 2010] Xuan Hong Dang and James Bai- ley. Generation of alternative clusterings using the cami approach. In SDM, pages 118-129, 2010.","title":"Generation of alternative clusterings using the cami approach","authors":"Xuan Hong, Dang, James Bailey","year":"2010","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Davidson and Qi, 2008] Ian Davidson and Zijie Qi. Finding alternative clusterings using constraints. In ICDM, pages 773-778, 2008.","title":"Finding alternative clusterings using constraints","authors":"Davidson, Qi, Ian Davidson, Zijie Qi","year":"2008","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ding et al., 2010] Chris HQ Ding, Tao Li, and Michael I Jordan. Convex and semi-nonnegative matrix factoriza- tions. TPAMI, 32(1):45-55, 2010.","title":"Convex and semi-nonnegative matrix factorizations","authors":"Tao Chris Hq Ding, Michael I Li, Jordan","year":"2010","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gao et al., 2015] Hongchang Gao, Feiping Nie, Xuelong Li, and Heng Huang. Multi-view subspace clustering. In ICCV, pages 4238-4246, 2015.","title":"Multi-view subspace clustering","authors":"Hongchang Gao, Feiping Nie, Xuelong Li, Heng Huang","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gönen and Alpaydın, 2011] Mehmet Gönen and Ethem Al- paydın. Multiple kernel learning algorithms. JMLR, 12(7):2211-2268, 2011.","title":"Multiple kernel learning algorithms","authors":"Mehmet Gönen, Ethem Alpaydın","year":"2011","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gretton et al., 2005] Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf. Measuring statisti- cal dependence with hilbert-schmidt norms. In ALT, pages 63-77, 2005.","title":"Measuring statistical dependence with hilbert-schmidt norms","authors":"Arthur Gretton, Olivier Bousquet, Alex Smola, Bernhard Schölkopf","year":"2005","venue":"ALT","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jain et al., 2008] Prateek Jain, Raghu Meka, and Inderjit S Dhillon. Simultaneous unsupervised learning of dis- parate clusterings. Statistical Analysis and Data Mining, 1(3):195-210, 2008.","title":"Simultaneous unsupervised learning of disparate clusterings","authors":"Prateek Jain, Raghu Meka, Inderjit S Dhillon","year":"2008","venue":"Statistical Analysis and Data Mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kumar and Daumé, 2011] Abhishek Kumar and Hal Daumé. A co-training approach for multi-view spectral clustering. In ICML, pages 393-400, 2011.","title":"A co-training approach for multi-view spectral clustering","authors":"Daumé Kumar, Abhishek Kumar, Hal Daumé","year":"2011","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2015] Yeqing Li, Feiping Nie, Heng Huang, and Junzhou Huang. Large-scale multi-view spectral cluster- ing via bipartite graph. In AAAI, 2015.","title":"Large-scale multi-view spectral clustering via bipartite graph","authors":"Li","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu et al., 2013] Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, and Yi Ma. Robust recovery of subspace structures by low-rank representation. TPAMI, 35(1):171-184, 2013.","title":"Robust recovery of subspace structures by low-rank representation","authors":"Liu","year":"2013","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Luo et al., 2018] Shirui Luo, Changqing Zhang, Wei Zhang, and Xiaochun Cao. Consistent and specific multi- view subspace clustering. In AAAI, pages 3730-3737, 2018.","title":"Consistent and specific multiview subspace clustering","authors":"Luo","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Mautz et al., 2018] Dominik Mautz, Wei Ye, Claudia Plant, and Christian Böhm. Discovering non-redundant k-means clusterings in optimal subspaces. In KDD, pages 1973- 1982, 2018.","title":"Discovering non-redundant k-means clusterings in optimal subspaces","authors":"Dominik Mautz, Wei Ye, Claudia Plant, Christian Böhm","year":"2018","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Monti et al., 2003] Stefano Monti, Pablo Tamayo, Jil- l Mesirov, and Todd Golub. Consensus clustering: a resampling-based method for class discovery and visu- alization of gene expression microarray data. Machine learning, 52(1-2):91-118, 2003.","title":"Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data","authors":"Stefano Monti, Pablo Tamayo, Jill Mesirov, Todd Golub","year":"2003","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Niu et al., 2010] Donglin Niu, Jennifer G Dy, and Michael I Jordan. Multiple non-redundant spectral clustering views. In ICML, pages 831-838, 2010.","title":"Multiple non-redundant spectral clustering views","authors":"Donglin Niu, Jennifer G Dy, Michael I Jordan","year":"2010","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tao et al., 2018] Hong Tao, Chenping Hou, Xinwang Liu, Tongliang Liu, Dongyun Yi, and Jubo Zhu. Reliable multi- view clustering. In AAAI, pages 4123-4130, 2018.","title":"Reliable multiview clustering","authors":"Tao","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tokuda et al., 2017] Tomoki Tokuda, Junichiro Yoshimo- to, Yu Shimizu, and et al. Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions. PLoS ONE, 12(10):e0186566, 2017.","title":"Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions","authors":"Tokuda","year":"2017","venue":"PLoS ONE","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2011] Hua Wang, Feiping Nie, Heng Huang, and Fillia Makedon. Fast nonnegative matrix tri- factorization for large-scale data co-clustering. In IJCAI, pages 1553-1558, 2011.","title":"Fast nonnegative matrix trifactorization for large-scale data co-clustering","authors":"Hua Wang, Feiping Nie, Heng Huang, Fillia Makedon","year":"2011","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2018] Xing Wang, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zhiwen Yu, and Zili Zhang. Mul- tiple co-clusterings. In ICDM, pages 1308-1313, 2018.","title":"Multiple co-clusterings","authors":"Xing Wang, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zhiwen Yu, Zili Zhang","year":"2018","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wang et al., 2019] Xing Wang, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Guoqiang Xiao, and Maozu Guo. Multiple independent subspace clusterings. In AAAI, pages 1-8, 2019.","title":"Multiple independent subspace clusterings","authors":"Xing Wang, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Guoqiang Xiao, Maozu Guo","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wright et al., 2010] John Wright, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas S Huang, and Shuicheng Yan. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 98(6):1031-1044, 2010.","title":"Sparse representation for computer vision and pattern recognition","authors":"John Wright, Yi Ma, Julien Mairal, Guillermo Sapiro, Thomas S Huang, Shuicheng Yan","year":"2010","venue":"Proceedings of the IEEE","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yang and Zhang, 2017] Sen Yang and Lijun Zhang. Non- redundant multiple clustering by nonnegative matrix fac- torization. Machine Learning, 106(5):695-712, 2017.","title":"Nonredundant multiple clustering by nonnegative matrix factorization","authors":"Sen Yang, Lijun Zhang","year":"2017","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Ye et al., 2016] Wei Ye, Samuel Maurus, Nina Hubig, and Claudia Plant. Generalized independent subspace cluster- ing. In ICDM, pages 569-578, 2016.","title":"Generalized independent subspace clustering","authors":"Wei Ye, Samuel Maurus, Nina Hubig, Claudia Plant","year":"2016","venue":"ICDM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}