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Consensus Kernel K-Means Clustering for Incomplete Multiview Data

Yongkai Ye, Xinwang Liu, Qiang Liu, Jianping Yin
2017 Computational Intelligence and Neuroscience  
In order to address this issue, we propose a new unified learning method for incomplete multiview clustering, which simultaneously imputes the incomplete views and learns a consistent clustering result  ...  Incomplete views are imputed to achieve an optimal clustering result in each view, while maintaining between-view consistency.  ...  for incomplete multiview clustering.  ... 
doi:10.1155/2017/3961718 pmid:29312448 pmcid:PMC5672121 fatcat:rakzsbd7jbbxzjiluhoz36bvyq

Incomplete Multiview Clustering via Late Fusion

Yongkai Ye, Xinwang Liu, Qiang Liu, Xifeng Guo, Jianping Yin
2018 Computational Intelligence and Neuroscience  
Most existing studies in the field of incomplete multiview clustering have focused on early fusion strategies, for example, learning subspace from multiple views.  ...  To this end, we propose a late fusion method for incomplete multiview clustering.  ...  A straightforward strategy for handling incomplete multiview clustering is to first fill the incomplete view information and then apply the common multiview clustering algorithm.  ... 
doi:10.1155/2018/6148456 fatcat:vnnrjyyppfbfhhy4qlh476v5aa

Adaptive Anchor-Based Partial Multiview Clustering

Xia Ji, Lei Yang, Sheng Yao
2020 IEEE Access  
As one of the most representative methods of multiview learning, multiview clustering can obtain better clustering results by exploring the consistency and complementarity of different views, and has been  ...  It is designed for complete multiview learning, meanwhile it is the foundation of the following partial multiview algorithms.  ... 
doi:10.1109/access.2020.3025881 fatcat:q2pdcdcpf5bbrev2nkzakijll4

PatentNet: A Large-Scale Incomplete Multiview, Multimodal, Multilabel Industrial Goods Image Database [article]

Fangyuan Lei, Da Huang, Jianjian Jiang, Ruijun Ma, Senhong Wang, Jiangzhong Cao, Yusen Lin, Qingyun Dai
2021 arXiv   pre-print
Through extensive experiments on image classification, image retrieval and incomplete multiview clustering, we demonstrate that our PatentNet is much more diverse, complex, and challenging, enjoying higher  ...  Nowadays, as the embodiment of innovation, the diversity of the industrial goods is significantly larger, in which the incomplete multiview, multimodal and multilabel are different from the traditional  ...  (%), Purity (%) of different incomplete multiview clustering methods on the chair subset of PatentNet.  ... 
arXiv:2106.12139v1 fatcat:t2eiq235uvfgni6ftyfiezdk7q

Automated Kernel Independent Component Analysis Based Two Variable Weighted Multi-view Clustering for Complete and Incomplete Dataset

M. Kalaiarasu, R. Radhakrishnan
2015 Research Journal of Applied Sciences Engineering and Technology  
(ACE) and Accuracy consistently in complete and incomplete view of the data in regard to the true clusters in the data.  ...  The proposed approach is competent for clustering complete and incomplete view dataset samples and moreovervalid for measuring the values of categorical, numerical and mixed data attributes.  ...  clustering for both complete and incomplete dataset.  ... 
doi:10.19026/rjaset.9.2611 fatcat:e5ffpc5nfvgeniejisuyklhjje

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

Weihua Ou, Fei Long, Yi Tan, Shujian Yu, Pengpeng Wang
2017 Multimedia tools and applications  
To address these problems, we propose a co-regularized multiview nonnegative matrix factorization method with correlation constraint for nonnegative representation learning, which jointly exploits consistent  ...  With the increasing availability of multiview nonnegative data in real applications, multiview representation learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions  ...  These methods are useful for the nonnegative multiview data analysis, however, they are not suitable for the noisy views and incomplete views, which are often encountered in real applications.  ... 
doi:10.1007/s11042-017-4926-0 fatcat:ooqq327ik5el5ofnrhklmohrqq

Incomplete Multi-view Clustering via Subspace Learning

Qiyue Yin, Shu Wu, Liang Wang
2015 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15  
In this paper, a novel incomplete multi-view clustering method is therefore developed, which learns unified latent representations and projection matrices for the incomplete multi-view data.  ...  Traditional multiview clustering methods usually assume that all examples have complete feature sets.  ...  CONCLUSION AND FUTURE WORK In this paper, we have proposed a novel incomplete multiview clustering algorithm to cluster incomplete multi-view data.  ... 
doi:10.1145/2806416.2806526 dblp:conf/cikm/YinWW15 fatcat:zpxqd7jl7vdadm5bc3edtb5a6a

Adversarial Incomplete Multi-view Clustering

Cai Xu, Ziyu Guan, Wei Zhao, Hongchang Wu, Yunfei Niu, Beilei Ling
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
To eliminate all these drawbacks, in this work we present an Adversarial Incomplete Multi-view Clustering (AIMC) method.  ...  However, in real-world datasets, it is often the case that a view may contain some missing data, resulting in the incomplete multi-view clustering problem.  ...  Conclusion In this paper, we proposed a Adversarial Incomplete Multiview Clustering (AIMC) method for IMC with an arbitrary number of views.  ... 
doi:10.24963/ijcai.2019/546 dblp:conf/ijcai/XuGZWNL19 fatcat:etoiyti34vekpeoc2ncpyiimjq

Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering

Anusua Trivedi, Piyush Rai, Hal Daumé, Scott L. Duvall
2012 ACM Transactions on Intelligent Systems and Technology  
In our approach, we consider page-text and tags as two separate views of the data, and learn a shared subspace that maximizes the correlation between the two views.  ...  We also discuss some possible future work including an active learning extension that can help in choosing which webpages to get tags for, if we only can get the social tags for only a small number of  ...  Acknowledgement This work is supported by resources and facilities of the VA Salt Lake City Health Care System with funding support from the Consortium for Healthcare Informatics Research (CHIR), VA HSR  ... 
doi:10.1145/2337542.2337552 fatcat:zknw75oor5geheog3aymmn6wwm

ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering [article]

Xiang Fang, Yuchong Hu, Pan Zhou, Dapeng Oliver Wu
2021 arXiv   pre-print
In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model.  ...  Multi-view clustering has wide applications in many image processing scenarios.  ...  As the first work for incomplete multi-view clustering, [40] proposes PVC to learn a common latent space from two incomplete views for clustering.  ... 
arXiv:2011.10331v3 fatcat:tcmr7sryq5eovmynlyir5ay4hy

One-Pass Incomplete Multi-view Clustering [article]

Menglei Hu, Songcan Chen
2019 arXiv   pre-print
Clustering on such multi-view datasets is called incomplete multi-view clustering (IMC) and quite challenging.  ...  To address this problem, in this paper, we propose an One-Pass Incomplete Multi-view Clustering framework (OPIMC).  ...  In this paper, we propose an One-Pass Incomplete Multiview Clustering framework (OPIMC) for large scale multiview datasets based on subspace learning.  ... 
arXiv:1903.00637v1 fatcat:3qnng5vkz5f4lbqnchu7y63v4u

Multi-view clustering: A survey

Yan Yang, Hao Wang
2018 Big Data Mining and Analytics  
namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering.  ...  Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods.  ...  [123, 124] studied incomplete multi-view learning for incomplete and unlabeled multi-view data.  ... 
doi:10.26599/bdma.2018.9020003 dblp:journals/bigdatama/YangW18 fatcat:jxfs7s5b2ndi3lyappfttchoim

Complete/incomplete multi‐view subspace clustering via soft block‐diagonal‐induced regulariser

Yongli Hu, Cuicui Luo, Boyue Wang, Junbin Gao, Yanfeng Sun, Baocai Yin
2021 IET Computer Vision  
This study proposes a novel multi-view soft block diagonal representation framework for clustering complete and incomplete multi-view data.  ...  Third, to handle incomplete multi-view data, multiple indicator matrices are utilised, which can mark the position of missing elements of each view.  ...  [47] make use of the consistency and inconsistency between multiple views, and learn an ideal similarity graph for all views. Cai et al.  ... 
doi:10.1049/cvi2.12077 fatcat:4aa7cpw46fevffmn5byi5xjtrm

Auto-Weighted Incomplete Multi-View Clustering

Wanyu Deng, Lixia Liu, Jianqiang Li, Yijun Lin
2020 IEEE Access  
For multiview clustering task, assigning a suitable weight for each view is beneficial to improve the clustering performance.  ...  for incomplete multi-view clustering. 5) From C.  ...  Author Name: Preparation of Papers for IEEE Access (February 2017) VOLUME XX, 2017  ... 
doi:10.1109/access.2020.3012500 fatcat:dsvd5zwe3fcazmaddpn6g4ozyu

Apple Variety Recognition Based on Multiview Feature Fusion

Jinjin Cai, Jie Li, Bo Liu, Wei Yao
2020 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
In the multiview classification stage, a robust multiview classification fusion algorithm is designed based on image block features generated by different descriptors for each view.  ...  The proposed method makes full use of the consistency and complementarity among different views to achieve the purpose of merging multiple views and jointly improving recognition performance.  ...  For example, consider the multiview learning method RMSC based on low-rank, sparse representation [17] . For V views, this method models every i views with a similarity matrix S (i) .  ... 
doi:10.18280/ria.340306 fatcat:czmd636cdzfnhjul3mtjdqfns4
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