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A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep  ...  Multi-view O2MAC Graph Clustering [69] One2multi graph autoencoder for multi-view graph clustering PPI Protein-Protein Interaction Progan: Network embedding via proximity generative ProGAN Proximity  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering [article]

Shi-Xun Lina, Guo Zhongb, Ting Shu
2020 arXiv   pre-print
Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other, to improve the clustering performance.  ...  To address the above issues, we propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific  ...  Multi-view Spectral Clustering via Integrating Nonnegative Embedding and Spectral Embedding (NESE) Table 4 : 4 Clstering results on the 100leaves dataset.  ... 
arXiv:2007.12829v1 fatcat:mbagkzkh6zekdmfvda4eiii5k4

Multiview Partitioning via Tensor Methods

Xinhai Liu, Shuiwang Ji, Wolfgang Glänzel, B. De Moor
2013 IEEE Transactions on Knowledge and Data Engineering  
In this paper, we present a novel tensor-based framework for integrating heterogeneous multi-view data in the context of spectral clustering.  ...  Index Terms-Multi-view clustering, tensor decomposition, spectral clustering, multi-linear singular value decomposition, higher-order orthogonal iteration X. Liu is with the  ...  De Lathauwer for deriving the version of HOOI with a single vector in one mode and for the theorem and proof in the Supplementary material 6. This work was supported by (1)  ... 
doi:10.1109/tkde.2012.95 fatcat:c3fzmbheh5fcphw33lciyyjavm

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
Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations.  ...  Finally, the basic partitions, weights, and consensus clustering are jointly learned in a unified framework.  ...  ZYGX2017KYQD177 and A03017023701012) and a 985 Project of UESTC (No. A1098531023601041) .  ... 
arXiv:1909.06008v1 fatcat:exttcqcagjaolaas6ilt5a5mhy

Multi-view Spectral Clustering on Conflicting Views [chapter]

Xiao He, Limin Li, Damian Roqueiro, Karsten Borgwardt
2017 Lecture Notes in Computer Science  
Here, we propose to overcome this problem by combining the ideas of multi-view spectral clustering with alternative clustering through kernel-based dimensionality reduction.  ...  In such cases, traditional multi-view clustering methods will not benefit from using multi-view data.  ...  Acknowledgments The authors greatly acknowledge Dean Bodenham for helpful discussions and proofreading of the manuscript.  ... 
doi:10.1007/978-3-319-71246-8_50 fatcat:2g6zmkfdjvbi3ey5qm7qztfw3u

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis [article]

Ye Liu, Lifang He, Bokai Cao, Philip S. Yu, Ann B. Ragin, Alex D. Leow
2018 arXiv   pre-print
Therefore, multi-view multi-graph embedding becomes a crucial task.  ...  Extensive experiments on real HIV and bipolar disorder brain network datasets demonstrate the superior performance of M2E on clustering brain networks by leveraging the multi-view multi-graph interactions  ...  IIS-1526499 and CNS-1626432, NIH grant No. R01-MH080636, and NSFC grants No. 61503253 and 61672313.  ... 
arXiv:1806.07703v1 fatcat:hv5b4unfczazrpzwfmtxrjsm3a

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

Ye Liu, Lifang He, Bokai Cao, Philip Yu, Ann Ragin, Alex Leow
2018 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Therefore, multi-view multi-graph embedding becomes a crucial task.  ...  Extensive experiments on real HIV and bipolar disorder brain network datasets demonstrate the superior performance of M2E on clustering brain networks by leveraging the multi-view multi-graph interactions  ...  (Kumar and Daumé 2011; Kumar, Rai, and Daume 2011) are the first works proposed to solve the multi-view clustering problem via spectral projection.  ... 
doi:10.1609/aaai.v32i1.11288 fatcat:klzplea4krh4dfdc5lg2i345sa

Multiple Graph Learning for Scalable Multi-view Clustering [article]

Tianyu Jiang, Quanxue Gao, Xinbo Gao
2021 arXiv   pre-print
To well exploit complementary information and tackle the scalability issue plaguing graph-based multi-view clustering, we propose an efficient multiple graph learning model via a small number of anchor  ...  Graph-based multi-view clustering has become an active topic due to the efficiency in characterizing both the complex structure and relationship between multimedia data.  ...  Hu et al. integrated nonnegative embedding and spectral embedding into a unified framework, and proposed multi-view spectral clustering method (SMSC) [16] . Xu et al.  ... 
arXiv:2106.15382v2 fatcat:uffafhrypreflgri6cg5lhixz4

Deep Multiple Auto-Encoder-Based Multi-view Clustering

Guowang Du, Lihua Zhou, Yudi Yang, Kevin Lü, Lizhen Wang
2021 Data Science and Engineering  
Multi-view clustering algorithms based on different theories have been proposed and extended in various applications.  ...  nonlinear structure information hidden in each view, and thus, the performance of multi-view clustering is weakened to a certain extent.  ...  This work was supported by the National Natural Science Foundation of China (61762090, 62062066, and 61966036), the Program for Innovation Research Team (in Science and Technology) in the University of  ... 
doi:10.1007/s41019-021-00159-z fatcat:dd4ml5u7dzf65ihdi4ryx5r6di

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.  ...  ACKNOWLEDGMENT This work was supported by National Institutes of Health (NIH) grants R01DA037349 and K02DA043063, and National Science Foundation (NSF) grants DBI-1356655, CCF-1514357, and IIS-1718738.  ... 
arXiv:1712.06246v2 fatcat:w3b2hfnqyzbbbfcz6t3gl5mlny

Multi-view Reconstructive Preserving Embedding for Dimension Reduction [article]

Huibing Wang and Lin Feng and Adong Kong and Bo Jin
2018 arXiv   pre-print
To address this problem, we propose a novel multi-view dimension reduction method named Multi-view Reconstructive Preserving Embedding (MRPE) in this paper.  ...  Furthermore, MRPE constructs an optimization problem and derives an iterative procedure to obtain the low-dimensional embedding.  ...  [12] developed a multi-view spectral clustering framework which achieves this goal by co-regularizing a clustering hypotheses, and proposed two co-regularization schemes to accomplish this.  ... 
arXiv:1807.10614v1 fatcat:h45qzaheirg27daomvpz2wifta

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.  ...  Guo and Ye [2] proposed a new algorithm named Anchor-based Partial Multi-view Clustering (APMC), which integrates the intrinsic and extrinsic view information into the fused similarities via anchors;  ... 
doi:10.1155/2021/4296247 fatcat:a632grlhdrfttfgiyaumkinjkm

Joint Clustering and Feature Selection [chapter]

Liang Du, Yi-Dong Shen
2013 Lecture Notes in Computer Science  
Specifically, we integrate Fisher score into the clustering framework.  ...  Inspired from the recent developments on discriminative clustering, we propose in this paper a novel unsupervised feature selection approach via Joint Clustering and Feature Selection (JCFS).  ...  Both of UDFS, FSSL and JELSR can viewed as an integration of embedding with different graphs and sparse subspace learning via ℓ 2,1 -norm regularization.  ... 
doi:10.1007/978-3-642-38562-9_25 fatcat:q7xmswrd6rexjh7t2rfuilp3kq

Learning Strictly Orthogonal p-Order Nonnegative Laplacian Embedding via Smoothed Iterative Reweighted Method

Haoxuan Yang, Kai Liu, Hua Wang, Feiping Nie
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
In addition, recent studies have shown that using the p-th order of the L2-norm distances in LE can find the best solution for clustering and promote the robustness of the embedding model against outliers  ...  In this work, we study LE that uses the p-th order of the L2-norm distances and satisfies both orthogonal and nonnegative constraints.  ...  Thus, mixed-sign solution is a generic difficulty for multi-way spectral clustering. To tackle this problem, a clustering task is performed after the embedding.  ... 
doi:10.24963/ijcai.2019/561 dblp:conf/ijcai/YangLWN19 fatcat:ze3yjcfqj5galccorxrea77rz4

JECL: Joint Embedding and Cluster Learning for Image-Text Pairs [article]

Sean T. Yang, Kuan-Hao Huang, Bill Howe
2020 arXiv   pre-print
Experiments show that JECL outperforms both single-view and multi-view methods on large benchmark image-caption datasets, and is remarkably robust to missing captions and varying data sizes.  ...  We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both representations and cluster  ...  and Deep Embedded Clustering (DEC) [2] . b) Multi-View Methods: We evaluate six state-of-the-art multi-view methods, including three multi-view representation learning models and three multi-view clustering  ... 
arXiv:1901.01860v3 fatcat:vqy4c2eejzedzajlxia44t6in4
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