ABSTRACT
For many real-world multimedia applications, data are often described by multiple views. Therefore, multi-view learning researches are of great significance. Traditional multi-view clustering methods assume that each view has complete data. However, missing data or partial data are more common in real tasks, which results in partial multi-view learning. Therefore, we propose a novel multi-view clustering method, called Partial Multi-view Subspace Clustering (PMSC), to address the partial multi-view problem. Unlike most existing partial multi-view clustering methods that only learn a new representation of the original data, our method seeks the latent space and performs data reconstruction simultaneously to learn the subspace representation. The learned subspace representation can reveal the underlying subspace structure embedded in original data, leading to a more comprehensive data description. In addition, we enforce the subspace representation to be non-negative, yielding an intuitive weight interpretation among different data. The proposed method can be optimized by the Augmented Lagrange Multiplier (ALM) algorithm. Experiments on one synthetic dataset and four benchmark datasets validate the effectiveness of PMSC under the partial multi-view scenario.
- Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, and Hua Zhang. 2015. Diversity-induced multi-view subspace clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 586--594.Google ScholarCross Ref
- Ehsan Elhamifar and Rene Vidal. 2013. Sparse subspace clustering: Algorithm, theory, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 35, 11 (2013), 2765--2781. Google ScholarDigital Library
- Hongchang Gao, Feiping Nie, Xuelong Li, and Heng Huang. 2015. Multi-view subspace clustering. In Proceedings of the IEEE International Conference on Computer Vision. 4238--4246. Google ScholarDigital Library
- Jun Guo and Wenwu Zhu. 2018. Partial multi-view outlier detection based on collective learning. In Proceedings of AAAI Conference on Artificial Intelligence. 298--305.Google Scholar
- Di Huang, Jia Sun, and Yunhong Wang. 2012. The buaa-visnir face database instructions. School Comput. Sci. Eng., Beihang Univ., Beijing, China, Tech. Rep. IRIP-TR-12-FR-001 (2012).Google Scholar
- Jun Li, Yu Kong, and Yun Fu. 2017. Sparse subspace clustering by learning approximation $ell _rm0 $ codes. In Proceedings of the AAAI Conference on Artificial Intelligence. 2189--2195.Google Scholar
- Shaoyuan Li, Yuan Jiang, and Zhihua Zhou. 2014. Partial multi-view clustering. In Proceedings of AAAI Conference on Artificial Intelligence. 1968--1974. Google ScholarDigital Library
- Tao Li, Mitsunori Ogihara, Wei Peng, Bo Shao, and Shenghuo Zhu. 2009. Music clustering with features from different information sources. IEEE Transactions on Multimedia , Vol. 11, 3 (2009), 477--485. Google ScholarDigital Library
- Zhouchen Lin, Risheng Liu, and Zhixun Su. 2011. Linearized alternating direction method with adaptive penalty for low-rank representation. In Proceedings of Advances in Neural Information Processing Systems. 612--620. Google ScholarDigital Library
- Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, and Yi Ma. 2013. Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 35, 1 (2013), 171--184. Google ScholarDigital Library
- Canyi Lu, Hai Min, Zhongqiu Zhao, Lin Zhu, Deshuang Huang, and Shuicheng Yan. 2012. Robust and efficient subspace segmentation via least squares regression. In Proceedings of the European Conference on Computer Vision. 347--360. Google ScholarDigital Library
- Canyi Lu, Hai Min, Zhongqiu Zhao, Lin Zhu, Deshuang Huang, and Shuicheng Yan. 2015. Structured sparse subspace clustering: A unified optimization framework. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 277--286.Google Scholar
- Andrew Y Ng, Michael I Jordan, and Yair Weiss. 2002. On spectral clustering: Analysis and an algorithm. In Proceedings of Advances in Neural Information Processing Systems. 849--856. Google ScholarDigital Library
- Feiping Nie, Guohao Cai, and Xuelong Li. 2017. Multi-view clustering and semi-supervised classification with adaptive neighbours. In Proceedings of AAAI Conference on Artificial Intelligence. 2408--2414.Google Scholar
- Chong Peng, Zhao Kang, and Qiang Cheng. 2017. Subspace clustering via variance regularized ridge regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4321--4330.Google ScholarCross Ref
- Weixiang Shao, Lifang He, and S Yu Philip. 2015. Multiple incomplete views clustering via weighted nonnegative matrix factorization with $L_2,1 $ regularization. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 318--334. Google ScholarDigital Library
- Bokun Wang, Yang Yang, Xing Xu, Alan Hanjalic, and Heng Tao Shen. 2017b. Adversarial cross-modal retrieval. In Proceedings of the ACM on Multimedia Conference . 154--162. Google ScholarDigital Library
- Qifan Wang, Luo Si, and Bin Shen. 2015b. Learning to hash on partial multi-modal data. In Proceedings of the International Joint Conference on Artificial Intelligence. 3904--3910. Google ScholarDigital Library
- Xiaobo Wang, Xiaojie Guo, Zhen Lei, Changqing Zhang, and Stan Z Li. 2017a. Exclusivity-consistency regularized multi-view subspace clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 923--931.Google ScholarCross Ref
- Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang, Qing Zhang, and Xiaodi Huang. 2015a. Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Transactions on Image Processing , Vol. 24, 11 (2015), 3939--3949.Google ScholarDigital Library
- Ming Yin, Junbin Gao, Zhouchen Lin, Qinfeng Shi, and Yi Guo. 2015a. Dual graph regularized latent low-rank representation for subspace clustering. IEEE Transactions on Image Processing , Vol. 24, 12 (2015), 4918--4933.Google ScholarDigital Library
- Qiyue Yin, Shu Wu, Ran He, and Liang Wang. 2015c. Multi-view clustering via pairwise sparse subspace representation. Neurocomputing , Vol. 156 (2015), 12--21. Google ScholarDigital Library
- Qiyue Yin, Shu Wu, and Liang Wang. 2015b. Incomplete multi-view clustering via subspace learning. In Proceedings of the ACM International on Conference on Information and Knowledge Management . 383--392. Google ScholarDigital Library
- Qiyue Yin, Shu Wu, and Liang Wang. 2017. Unified subspace learning for incomplete and unlabeled multi-view data. Pattern Recognition , Vol. 67 (2017), 313--327. Google ScholarDigital Library
- Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, and Xiaochun Cao. 2015. Low-rank tensor constrained multiview subspace clustering. In Proceedings of the IEEE International Conference on Computer Vision. 1582--1590. Google ScholarDigital Library
- Changqing Zhang, Qinghua Hu, Huazhu Fu, Pengfei Zhu, and Xiaochun Cao. 2017. Latent multi-view subspace clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4279--4287.Google ScholarCross Ref
- Handong Zhao, Zhengming Ding, and Yun Fu. 2017. Multi-view clustering via deep matrix factorization. In Proceedings of AAAI Conference on Artificial Intelligence. 2921--2927.Google Scholar
- Handong Zhao and Yun Fu. 2015. Dual-regularized multi-view outlier detection. In Proceedings of the International Joint Conference on Artificial Intelligence. 4077--4083. Google ScholarDigital Library
- Handong Zhao, Hongfu Liu, and Yun Fu. 2016a. Incomplete multi-modal visual data grouping. In Proceedings of the International Joint Conference on Artificial Intelligence. 2392--2398. Google ScholarDigital Library
- Zhou Zhao, Hanqing Lu, Deng Cai, Xiaofei He, and Yueting Zhuang. 2016b. Partial multi-modal sparse coding via adaptive similarity structure regularization. In Proceedings of the ACM on Multimedia Conference . 152--156. Google ScholarDigital Library
Index Terms
- Partial Multi-view Subspace Clustering
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