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A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering

Jing Liu, Fuyuan Cao, Xiao-Zhi Gao, Liqin Yu, Jiye Liang
In this paper, we propose a cluster-weighted kernel k-means method for multi-view clustering.  ...  The cluster labels are learned simultaneously with the cluster weights in an alternative updating way, by minimizing the weighted sum-of-squared errors of the kernel k-means.  ...  Weighted Multi-View Kernel K-Means Revisit The kernel k-means can be used in multi-view clustering. Tzortzis and Likas (2012) proposed a weighted multi-view kernel k-means clustering method.  ... 
doi:10.1609/aaai.v34i04.5922 fatcat:uit6rn64dnh7rp76eajyywohuy

Iterative Weight Updated Kernel k-Means for Multi-View Clustering

Kishan, Kiran
2016 South Asian Journal of Engineering and Technology   unpublished
This paper presents a method for clustering the multi-view data.  ...  We applied this method for some of the available multi-view data sets and are given better performance than the available multi-view clustering algorithms.  ...  Conclusion This work presents an Iterative weight updated Kernel k-Means algorithm for Multi-view clustering on Iris Dataset and Multiple Features Dataset.  ... 

A feature-reduction multi-view k-means clustering algorithm

Miin-Shen Yang, Kristina P. Sinaga
2019 IEEE Access  
INDEX TERMS Clustering, k-means, multi-view k-means, feature-reduction learning, feature-reduction multi-view k-means (FRMVK).  ...  A new multi-view k-means objective function is firstly proposed for constructing the learning mechanism for feature weights in multiview clustering.  ...  Feature-weighted techniques had been used for multi-view k-means clustering algorithms, such as simultaneous weighting on views and features (SWVF) [12] and weighted multi-view clustering with feature  ... 
doi:10.1109/access.2019.2934179 fatcat:rkctsd62jvc5pb3dowxe3bxcfe

Multi-View Fuzzy Clustering with The Alternative Learning between Shared Hidden Space and Partition [article]

Zhaohong Deng, Chen Cui, Peng Xu, Ling Liang, Haoran Chen, Te Zhang, Shitong Wang
2019 arXiv   pre-print
This method is based on the classical fuzzy c-means clustering model, and obtains associ-ated information between different views by introducing shared hidden space.  ...  The experimental result shows that the proposed multi-view clustering method has better performance than many related clustering methods.  ...  Multi-view clustering methods based on single view clustering methods, such as K-means [7, 8] , Fuzzy C-means (FCM) [9, 10] , Maximum Entropy Clustering (MEC) [11, 12] and Possibilistic C-means (PCM  ... 
arXiv:1908.04771v1 fatcat:7tpjoianf5dnplvfuuuwxnynhm

New Approaches in Multi-View Clustering [chapter]

Fanghua Ye, Zitai Chen, Hui Qian, Rui Li, Chuan Chen, Zibin Zheng
2018 Recent Applications in Data Clustering  
learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning.  ...  Recently, multi-view clustering has achieved great success in various applications.  ...  Basic form of multi-view k-means Both the k-means and the kernel k-means described above are designed for single-view data.  ... 
doi:10.5772/intechopen.75598 fatcat:jniifuf4ync27fofz4fpbnfiia

Semi-Supervised Multi-View Clustering with Weighted Anchor Graph Embedding

Senhong Wang, Jiangzhong Cao, Fangyuan Lei, Qingyun Dai, Shangsong Liang, Bingo Wing-Kuen Ling, Cesar F. Caiafa
2021 Computational Intelligence and Neuroscience  
In view of these problems, this paper proposes a novel framework called Semi-supervised Multi-View Clustering with Weighted Anchor Graph Embedding (SMVC_WAGE), which is conceptually simple and efficiently  ...  A number of literature reports have shown that multi-view clustering can acquire a better performance on complete multi-view data.  ...  the cluster centroids of semi-supervised K-Means.  ... 
doi:10.1155/2021/4296247 fatcat:a632grlhdrfttfgiyaumkinjkm

Kernel-Based Weighted Multi-view Clustering

Grigorios Tzortzis, Aristidis Likas
2012 2012 IEEE 12th International Conference on Data Mining  
Two efficient iterative algorithms are proposed that alternate between updating the view weights and recomputing the clusters to optimize the intra-cluster variance from different perspectives.  ...  The conducted experiments reveal the effectiveness of our methodology compared to other multi-view methods.  ...  They are called multi-view kernel k-means (MVKKM) and multi-view spectral clustering (MVSpec) respectively. 1) Updating the clusters for given weights -MVKKM algorithm: When the weights w v are known,  ... 
doi:10.1109/icdm.2012.43 dblp:conf/icdm/TzortzisL12 fatcat:tebybdhwxzcqjksue26eoqtc6m

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

Yangtao Wang, Lihui Chen
2016 arXiv   pre-print
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.  ...  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.  ...  Two hard clustering approaches are a K-means based multi-view clustering and the minimax optimization based multi-view spectral clustering.  ... 
arXiv:1608.07005v1 fatcat:asg4ntn4i5egzpm6dnz3fvg33u

Discriminatively Embedded K-Means for Multi-view Clustering

Jinglin Xu, Junwei Han, Feiping Nie
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
To address this problem, this paper proposes a novel multi-view clustering method called Discriminatively Embedded K-Means (DEKM), which embeds the synchronous learning of multiple discriminative subspaces  ...  In this framework, we firstly design a weighted multi-view Linear Discriminant Analysis (LDA), and then develop an unsupervised optimization scheme to alternatively learn the common clustering indicator  ...  Most of these methods can be roughly classified into two categories: Multi-View K-Means Clustering (MVKM) and Multi-View Spectral Clustering (MVSC).  ... 
doi:10.1109/cvpr.2016.578 dblp:conf/cvpr/XuHN16 fatcat:nm4uchlttrf3zffj2rhkzs3b4u

Epileptic EEG Detection Using a Multi-view Fuzzy Clustering Algorithm with Multi-medoid

Qianyi Zhan, Yizhang Jiang, Kaijian Xia, Jing Xue, Wei Hu, Xinghuang Lin, Yuan Liu
2019 IEEE Access  
Considering that the multiview learning mechanism combines information from each view to improve the algorithm's clustering performance, a multi-view fuzzy clustering algorithm with multi-medoid (MvFMMdd  ...  INDEX TERMS Epileptic EEG, multi-view, multi-medoid, fuzzy clustering. KAIJIAN XIA received the M.S. degree from Jiangnan University, Wuxi, China, in 2010.  ...  [35] proposed a multi-view fuzzy c-means (MvFCM) algorithm based on the classic FCM. Chen et al. [36] upgraded the traditional k-means algorithm to a multi-view k-means algorithm. III.  ... 
doi:10.1109/access.2019.2947689 fatcat:xni2nfru4nfbxkflssq6ntt7xa

TW-Co-MFC: Two-level weighted collaborative fuzzy clustering based on maximum entropy for multi-view data

Jie Hu, Yi Pan, Tianrui Li, Yan Yang
2021 Tsinghua Science and Technology  
In this study, we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering (TW-Co-MFC) approach to address the aforementioned issues.  ...  to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for clustering.  ...  [8] presented a strategy of simultaneous weighting of views and features to accomplish a multi-view clustering task under the classical k-means framework.  ... 
doi:10.26599/tst.2019.9010078 fatcat:fwjycta7aba7bi3yhzf5fj4gam

A Survey on Multi-View Clustering [article]

Guoqing Chao, Shiliang Sun, Jinbo Bi
2018 arXiv   pre-print
We further discuss the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning.  ...  Although recently, multi-view clustering (MVC) methods have been developed rapidly, there has not been a survey to summarize and analyze the current progress.  ...  To extend fuzzy clustering method to multiview clustering, each view is weighted and the multi-view versions of fuzzy c-means and fuzzy k-means are obtained in [42] and [51] , respectively. E.  ... 
arXiv:1712.06246v2 fatcat:w3b2hfnqyzbbbfcz6t3gl5mlny

A Self-Organizing Tensor Architecture for Multi-View Clustering [article]

Lifang He, Chun-ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, Fei Wang
2018 arXiv   pre-print
Although several multi-view clustering methods have been proposed, most of them routinely assume one weight for one view of features, and thus inter-view correlations are only considered at the view-level  ...  Specifically, we propose a multi-linear multi-view clustering (MMC) method that can efficiently explore the full-order structural information among all views and reveal the underlying subspace structure  ...  Most of existing multi-view clustering algorithms are essentially extended from classical single-view clustering algorithms, such as spectral clustering and K-means clustering.  ... 
arXiv:1810.07874v1 fatcat:7b2cxamlbbftxl7gkypnct4xr4

A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data

Shan Zeng, Xiuying Wang, Hui Cui, Chaojie Zheng, David Feng
2018 IEEE transactions on fuzzy systems  
We firstly construct a common multi-kernel space (CMKS) from a set of basis kernels to better reflect clustering information of each individual view.  ...  Clustering is increasingly important for multiview data analytics and current algorithms are either based on the collaborative learning of local partitions or directly derived global clustering from multi-kernel  ...  clustering algorithm (TW-k-means) [12] and 6) weighted view collaborative fuzzy c-means algorithm (WV-Co-FCM) [25] .  ... 
doi:10.1109/tfuzz.2017.2743679 fatcat:4uqr5dy5bvbgnao53kymd3rzim

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.  ...  At the same time, kernel k-means algorithm is applied to obtain the final result. • Robust multi-view K-means clustering (RMKMC) [65] : It adopts 21norm in traditional k-means algorithm to deal with data  ... 
arXiv:1909.06940v1 fatcat:7kngor4tgfdvxbpkafc2se4l3e
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