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Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data [chapter]

Evgeni Tsivtsivadze, Hanneke Borgdorff, Janneke van de Wijgert, Frank Schuren, Rita Verhelst, Tom Heskes
2013 Lecture Notes in Computer Science  
We propose a novel algorithm that is based on neighborhood co-regularization of the clustering hypotheses and that searches for the solution which is consistent across different views.  ...  By leveraging information from multiple views we can obtain clustering that is more robust and accurate compared to the one obtained via the individual views.  ...  Our work extends the spectral clustering method [8, 9] to a multi-view setting.  ... 
doi:10.1007/978-3-642-40705-5_8 fatcat:igyhmgofkvh2xjota4ucfi2tqm

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-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
To fill these gaps, in this paper, we propose a novel multi-view spectral clustering model which performs graph fusion and spectral clustering simultaneously.  ...  A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data.  ...  Consequently, the clusterings of multiple views tend towards consensus. • Co-regularized multi-view spectral clustering (Co-reg) [16] : This method employs co-regularization technique to make the clusterings  ... 
arXiv:1909.06940v1 fatcat:7kngor4tgfdvxbpkafc2se4l3e

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

Tyng-Luh Liu
2017 arXiv   pre-print
Owing to the effectiveness of spectral clustering, many multi-view clustering methods are based on it.  ...  In this work, we propose a novel multi-view spectral clustering method for large-scale data.  ...  In [11] , they employ two co-regularization strategies, pairwise and centroid regularization, to develop two multi-view spectral clustering schemes.  ... 
arXiv:1707.09866v1 fatcat:3t3kxud26fcj3mhr4xhtvvmniy

Multi-view Spectral Clustering via ELM-AE Ensemble Features Representations Learning

Lijuan Wang, Shifei Ding
2020 IEEE Access  
Kumar et al. introduced the co-regularization method to SC and proposed a co-regularized framework [8] .  ...  The second step was to apply the standard spectral clustering algorithm to the embedded features. And, we introduce the co-regularization method [8] to the MvSC-EF-ELM.  ...  Conclusion In this paper, we proposed MvSC-EF-ELM algorithm to apply the ELM-AE into feature learning and take advantage of the co-regularization mechanism to multi-view spectral clustering, which is feasible  ... 
doi:10.1109/access.2020.3034624 fatcat:h62wgcudivdyjexdelg2pqlubq

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.  ...  We further discuss the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning.  ...  Employing the co-training and co-regularization ideas, Kumar et al. [6] , [19] proposed co-training multi-view clustering and co-regularization multi-view clustering, respectively.  ... 
arXiv:1712.06246v2 fatcat:w3b2hfnqyzbbbfcz6t3gl5mlny

Multi-view clustering via pairwise sparse subspace representation

Qiyue Yin, Shu Wu, Ran He, Liang Wang
2015 Neurocomputing  
Multi-view clustering, which aims to cluster datasets with multiple sources of information, has a wide range of applications in the communities of data mining and pattern recognition.  ...  An alternating minimization method is provided as an efficient solution for the proposed multi-view clustering algorithm.  ...  This further validates our proposed multi-view clustering framework. Compared with MultiSSC_1, MultiSSC uses an l 1 -norm based co-regularization instead of the l 2 -norm co-regularization.  ... 
doi:10.1016/j.neucom.2015.01.017 fatcat:hs2oas7p7ffpflquiw36grlx4i

Motion Segmentation by Exploiting Complementary Geometric Models [article]

Xun Xu, Loong-Fah Cheong, Zhuwen Li
2018 arXiv   pre-print
From these considerations, we propose a multi-view spectral clustering framework that synergistically combines multiple models together.  ...  We first review the single view spectral clustering problem and then extend it to multi-view clustering.  ...  Subset Constrained Multi-View Spectral Clustering The above two multi-view spectral clustering schemes are generic fusion methods that do not exploit any relation that might exist between the different  ... 
arXiv:1804.02142v1 fatcat:mm73kuejybawld574owm4acezu

Motion Segmentation by Exploiting Complementary Geometric Models

Xun Xu, Loong Fah Cheong, Zhuwen Li
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
From these considerations, we propose a multi-view spectral clustering framework that synergistically combines multiple models together.  ...  We first review the single view spectral clustering problem and then extend it to multi-view clustering.  ...  Subset Constrained Multi-View Spectral Clustering The above two multi-view spectral clustering schemes are generic fusion methods that do not exploit any relation that might exist between the different  ... 
doi:10.1109/cvpr.2018.00302 dblp:conf/cvpr/XuCL18 fatcat:p3np7nrwtbhwre56ovrywxpj3y

Flexible and Robust Multi-Network Clustering

Jingchao Ni, Hanghang Tong, Wei Fan, Xiang Zhang
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
Various multi-view and multi-domain graph clustering methods have recently been developed to integrate multiple networks. In these methods, a network is treated as a view or domain.  ...  Our method models the domain similarity as a network, which can be utilized to regularize the clustering structures in different networks.  ...  (CTSC) [19] ; (4) multi-view pairwise co-regularized spectral clustering (PairCRSC) [20] ; (5) multi-view centroid-based co-regularized spectral clustering (CentCRSC) [20] ; (6) Tensor Factorization  ... 
doi:10.1145/2783258.2783262 dblp:conf/kdd/NiTFZ15 fatcat:fm6cczgnzbdc5lyakfdoclzc24

Exclusivity-Consistency Regularized Multi-view Subspace Clustering

Xiaobo Wang, Xiaojie Guo, Zhen Lei, Changqing Zhang, Stan Z. Li
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups.  ...  To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different  ...  The co-regularized multi-view spectral clustering introduced in [15] is to perform clustering on different views simultaneously with a co-regularization constraint.  ... 
doi:10.1109/cvpr.2017.8 dblp:conf/cvpr/WangGLZL17 fatcat:uf33dc3bovfmvcgcmzhjey57jy

Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification

Wei Di, Melba M. Crawford
2011 IEEE Journal on Selected Topics in Signal Processing  
The first regularizer explores the intrinsic multi-view information embedded in the hyperspectral data.  ...  A novel co-regularization framework for active learning is proposed for hyperspectral image classification.  ...  Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification Wei Di, Student Member, IEEE, and Melba M.  ... 
doi:10.1109/jstsp.2011.2123077 fatcat:rqv53imawvde3ozcwqjfihzhzi

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 co-regularized spectral clustering, although a state of the art, performed similarly to the single view biclustering.  ... 
doi:10.1109/bibm.2014.6999290 dblp:conf/bibm/BiSXLMQ14 fatcat:hbgxbp3edneb3fxe6balpppuki

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  
Recently, multi-view clustering has achieved great success in various applications.  ...  To provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, this chapter summarizes five kinds of popular clustering methods and their multi-view  ...  For example, the co-training 1 and co-regularization 2 methods of classical multi-view spectral clustering are open in GitHub with MATLAB.  ... 
doi:10.5772/intechopen.75598 fatcat:jniifuf4ync27fofz4fpbnfiia

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.  ...  on concatenated features is used as a baseline algorithm. • Co-regularized multi-view spectral clustering (Co-reg) [25] : A co-regularization mechnism is utilized to ensure that partitions from different  ... 
arXiv:1912.01201v1 fatcat:v6yj6mnycbajdc5ajby35gt2qy
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