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Guided Co-training for Large-Scale Multi-View Spectral Clustering [article]

Tyng-Luh Liu
2017 arXiv   pre-print
In this work, we propose a novel multi-view spectral clustering method for large-scale data.  ...  Our approach is structured under the guided co-training scheme to fuse distinct views, and uses the sampling technique to accelerate spectral clustering.  ...  View In [13] , a guided co-training approach is established for multiview spectral clustering.  ... 
arXiv:1707.09866v1 fatcat:3t3kxud26fcj3mhr4xhtvvmniy

Multiple-View Spectral Embedded Clustering Using a Co-training Approach [chapter]

Hong Tao, Chenping Hou, Dongyun Yi
2013 Lecture Notes in Electrical Engineering  
We derive a co-training algorithm to obtain a most informative clustering by iteratively modifying the affinity graph used for one view using the discriminative information from the other views.  ...  It is a challenging task to integrate multi-view representations, each of which is of high dimension to improve the clustering performance.  ...  Spectral Embedded Clustering Using a Co-training Approach 981 Multiple-View Spectral Embedded Clustering Using a Co-training Approach Multiple-View Spectral Embedded Clustering Using a Co-training  ... 
doi:10.1007/978-3-319-01766-2_112 fatcat:mnjvz4hinzg43eihn5dlzjyz7q

Multi-graph Fusion for Multi-view Spectral Clustering [article]

Zhao Kang and Guoxin Shi and Shudong Huang and Wenyu Chen and Xiaorong Pu and Joey Tianyi Zhou and Zenglin Xu
2019 arXiv   pre-print
A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data.  ...  To fill these gaps, in this paper, we propose a novel multi-view spectral clustering model which performs graph fusion and spectral clustering simultaneously.  ...  That is to say, we assume that all the views are of the same importance to the clustering task. • Co-training multi-view spectral clustering (Co-train) [15] : It utilizes the eigenvector from one view  ... 
arXiv:1909.06940v1 fatcat:7kngor4tgfdvxbpkafc2se4l3e

Federated Multi-view Spectral Clustering

Hongtao Wang, Ang Li, Bolin Shen, Yuyan Sun, Hongmei Wang.
2020 IEEE Access  
Co-reg refers to Co-Regularized multi-view spectral clustering approach proposed by [8] . Our work is based on Co-reg and extends it to the federated scenario.  ...  CONCLUSION In this paper, we propose a novel approach named Federated Multi-view Spectral Clustering (FMSC) to address the possible privacy leakage problem in centralized Multi-view Spectral Clustering  ... 
doi:10.1109/access.2020.3036747 fatcat:kiqxdnrkavdz3dga5w7olmjim4

A Survey on Multi-View Clustering [article]

Guoqing Chao, Shiliang Sun, Jinbo Bi
2018 arXiv   pre-print
Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention.  ...  Therefore, this paper reviews the common strategies for combining multiple views of data and based on this summary we propose a novel taxonomy of the MVC approaches.  ...  Refer to [65] for more details about spectral clustering. 2) Co-Training Multi-View Spectral Clustering: For semisupervised learning, co-training with two views has been a widely recognized idea when  ... 
arXiv:1712.06246v2 fatcat:w3b2hfnqyzbbbfcz6t3gl5mlny

Multiple Partitions Aligned Clustering [article]

Zhao Kang and Zipeng Guo and Shudong Huang and Siying Wang and Wenyu Chen and Yuanzhang Su and Zenglin Xu
2019 arXiv   pre-print
Moreover, a weight is assigned for each view to account for the clustering capacity differences of views.  ...  We demonstrate the effectiveness of our approach on several real datasets, where significant improvement is found over other state-of-the-art multi-view clustering methods.  ...  ZYGX2017KYQD177 and A03017023701012) and a 985 Project of UESTC (No. A1098531023601041) .  ... 
arXiv:1909.06008v1 fatcat:exttcqcagjaolaas6ilt5a5mhy

A survey of multi-view machine learning

Shiliang Sun
2013 Neural computing & applications (Print)  
This paper reviews theories developed to understand the properties and behaviors of multi-view learning, and gives a taxonomy of approaches according to the machine learning mechanisms involved and the  ...  This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research.  ...  [24] further proposed two co-regularization based approaches for multi-view spectral clustering by enforcing the clustering hypotheses on different views to agree with each other.  ... 
doi:10.1007/s00521-013-1362-6 fatcat:kzt7hibfo5axheedlaofw3pb7m

Automatic Social Circle Detection Using Multi-View Clustering

Yuhao Yang, Chao Lan, Xiaoli Li, Bo Luo, Jun Huan
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
We propose a one-side co-trained spectral clustering algorithm, which is tailored for the sparse nature of social network data. We also propose two evaluation measurements.  ...  We evaluate our approach on ego networks of twitter users, and compare the proposed technique with single-view clustering and original co-trained spectral clustering techniques.  ...  To better utilize such properties, we present a Selective Co-Trained Spectral Clustering (SCSC) algorithm for multi-view clustering.  ... 
doi:10.1145/2661829.2661973 dblp:conf/cikm/YangLLLH14 fatcat:3cwl366b4jgore3hfp5ys5odda

Diversity-induced Multi-view Subspace Clustering

Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task.  ...  In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features.  ...  •Co-Training SPC [16] . The co-training based multiview spectral clustering method assumes that the true underlying clustering would assign a point to the same cluster irrespective of the view.  ... 
doi:10.1109/cvpr.2015.7298657 dblp:conf/cvpr/CaoZFLZ15 fatcat:o75se56quzgjfko3lxwtzkqgji

A sparse integrative cluster analysis for understanding soybean phenotypes

Jinbo Bi, Jiangwen Sun, Tingyang Xu, Jin Lu, Yansong Ma, Lijuan Qiu
2014 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
Comparison with several latest multi-view co-clustering methods demonstrates the superior performance of the proposed approach.  ...  We employ a new multi-view singular value decomposition approach that simultaneously decomposes the data matrix gathered at each location into sparse singular vectors.  ...  The different methods include the proposed multi-view bicluster (MVB), single view biclustering (SVB), co-trained spectral (CTS) clustering, co-regulaized spectral (CRS) clustering, kernel addition (KA  ... 
doi:10.1109/bibm.2014.6999290 dblp:conf/bibm/BiSXLMQ14 fatcat:hbgxbp3edneb3fxe6balpppuki

Multi-View Clustering in Latent Embedding Space

Man-Sheng Chen, Ling Huang, Chang-Dong Wang, Dong Huang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while  ...  Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach.  ...  Robust Multi-view Spectral Clustering (RMSC) (Xia et al. 2014 ): It is a robust multi-view spectral clustering method which uses the standard Markov chain for clustering. 7.  ... 
doi:10.1609/aaai.v34i04.5756 fatcat:4nwwoxltifcvlpq2pvhajqlsta

Non-Linear Fusion for Self-Paced Multi-View Clustering [article]

Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He
2021 arXiv   pre-print
In this paper, inspired by the effectiveness of non-linear combination in instance learning and the auto-weighted approaches, we propose Non-Linear Fusion for Self-Paced Multi-View Clustering (NSMVC),  ...  With the advance of the multi-media and multi-modal data, multi-view clustering (MVC) has drawn increasing attentions recently.  ...  In co-training approach for multi-view spectral clustering (co-train) [16] and co-regularized multi-view spectral clustering (co-reg) [17] , Kumar et al. firstly put forward the fundamental assumption  ... 
arXiv:2104.09255v1 fatcat:6j2rvteq4fdwpc77he27c5uaeu

Multi-view Subspace Clustering via Partition Fusion [article]

Juncheng Lv and Zhao Kang and Boyu Wang and Luping Ji and Zenglin Xu
2019 arXiv   pre-print
Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result.  ...  Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way.  ...  views are close to each other. • Co-trained multi-view spectral clustering (Co-train) [24] : A co-training approach is used to learn multiple Laplacian eigenspace. • Multi-view kernel k-means clustering  ... 
arXiv:1912.01201v1 fatcat:v6yj6mnycbajdc5ajby35gt2qy

Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering [article]

Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan
2016 arXiv   pre-print
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.  ...  spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view  ...  [Kumar et al., 2011] proposed a state-of-the-art co-regularized spectral clustering for multi-view data.  ... 
arXiv:1608.05560v1 fatcat:5wyey5gvgbfshg2b5fraogn6gm

Multi-View Fuzzy Clustering with Minimax Optimization for Effective Clustering of Data from Multiple Sources [article]

Yangtao Wang, Lihui Chen
2016 arXiv   pre-print
We observed that MinimaxFCM outperforms related multi-view clustering approaches in terms of clustering accuracy, demonstrating the great potential of MinimaxFCM for multi-view data analysis.  ...  In this paper, we propose a new multi-view fuzzy clustering approach called MinimaxFCM by using minimax optimization based on well-known Fuzzy c means.  ...  ., 2014 ) is a multi-view spectral clustering approach based on minimax optimization.  ... 
arXiv:1608.07005v1 fatcat:asg4ntn4i5egzpm6dnz3fvg33u
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