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Essential Tensor Learning for Multi-view Spectral Clustering [article]

Jianlong Wu, Zhouchen Lin, Hongbin Zha
2019 arXiv   pre-print
In this paper, we focus on the Markov chain based spectral clustering method and propose a novel essential tensor learning method to explore the high order correlations for multi-view representation.  ...  Multi-view clustering attracts much attention recently, which aims to take advantage of multi-view information to improve the performance of clustering.  ...  Yuan Xie for his selfless support in sharing codes and datasets as well as the valuable suggestions.  ... 
arXiv:1807.03602v2 fatcat:nute6fz6t5fuvojg5trnlwhpxe

Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering [article]

Qinghai Zheng, Jihua Zhu, Zhongyu Li, Haoyu Tang, Shuangxun Ma
2020 arXiv   pre-print
To address this problem, we propose a novel Tensor-based Intrinsic Subspace Representation Learning (TISRL) for multi-view clustering in this paper.  ...  As a hot research topic, many multi-view clustering approaches are proposed over the past few years.  ...  ETLMSC 29 is an essential tensor learning for multi-view spectral clustering, which imposes a t-SVD-based nuclear norm on the transition probability matrices of all views to learn the essential tensor  ... 
arXiv:2010.09193v6 fatcat:xte65n6aszbwplggvg76lani2e

Unified Graph and Low-Rank Tensor Learning for Multi-View Clustering

Jianlong Wu, Xingyu Xie, Liqiang Nie, Zhouchen Lin, Hongbin Zha
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Towards these two issues, in this paper, we propose the unified graph and low-rank tensor learning (UGLTL) for multi-view clustering.  ...  Multi-view clustering aims to take advantage of multiple views information to improve the performance of clustering.  ...  Conclusions In this paper, we propose a novel unified graph and lowrank tensor learning for multi-view clustering. View-specific affinity matrix is learned based on projected graph learning.  ... 
doi:10.1609/aaai.v34i04.6109 fatcat:3olxmvzlcbaq5iqejvcv7qjgby

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

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  ...  Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm  ...  To well characterizes high-order information embedded in view-similar graph, Wu et al. [14] proposed essential tensor learning for multi-view spectral clustering (ETLMSC) method.  ... 
arXiv:2106.15382v2 fatcat:uffafhrypreflgri6cg5lhixz4

Robust Kernelized Multiview Clustering Based on High-Order Similarity Learning

Yanying Mei, Zhenwen Ren, Bin Wu, Tao Yang, Yanhua Shao
2022 IEEE Access  
INDEX TERMS Multi-view clustering, high-order similarity, low-rank tensor learning, kernel method.  ...  This paper explores the robust kernelized multi-view clustering (MVC) for nonlinear data.  ...  tensor learning for MVSC (ETLMSC) is a novel tensor-based spectral clustering method by using the multi-view transition probability matrices of the Markov chain to excavate the high-order relations of  ... 
doi:10.1109/access.2022.3176436 fatcat:vkwce3tg3bhxrd73fnruus65ae

Tensor-based Multi-view Spectral Clustering via Shared Latent Space [article]

Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A.K. Suykens
2022 arXiv   pre-print
Multi-view Spectral Clustering (MvSC) attracts increasing attention due to diverse data sources.  ...  In our method, the dual variables, playing the role of hidden features, are shared by all views to construct a common latent space, coupling the views by learning projections from view-specific spaces.  ...  jointly supported by ERC Advanced Grant E-DUALITY (787960), KU Leuven Grant CoE PFV/10/002, and Grant FWO G0A4917N, EU H2020 ICT-48 Network TAILOR (Foundations of Trustworthy AI -Integrating Reasoning, Learning  ... 
arXiv:2207.11559v1 fatcat:7rqz6fn35ngl3mgxlvjvzkrkp4

Effective and Efficient Graph Learning for Multi-view Clustering [article]

Quanxue Gao, Wei Xia, Xinbo Gao, Xiangdong Zhang, Qin Li, Dacheng Tao
2022 arXiv   pre-print
In this paper, drawing the inspiration from the bipartite graph, we propose an effective and efficient graph learning model for multi-view clustering.  ...  Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers and AE for their constructive comments and suggestions.  ... 
arXiv:2108.06734v2 fatcat:vr26t2vdgvg5fjhtmnrgyiwmke

Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation [article]

Yuheng Jia, Hui Liu, Junhui Hou, Sam Kwong, Qingfu Zhang
2020 arXiv   pre-print
This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling.  ...  Specifically, we explicitly impose a symmetric low-rank constraint and a structured sparse low-rank constraint on the frontal and horizontal slices of the tensor to characterize the intra-view and inter-view  ...  Thus, many multiview spectral clustering (MVSC) methods were proposed [3] for processing such multi-view data. For example, Xia et al.  ... 
arXiv:2004.14705v2 fatcat:7wabi5sc3bhkzbmqch4qkn5vre

Robust Multiview Subspace Clustering of Images via Tighter Rank Approximation

Xiaoli Sun, Youjuan Wang, Ming Yang, Xiujun Zhang
2021 IEEE Access  
In this paper, we focus on the multi-view subspace clustering problem under the framework of third order tensor.  ...  Recently, tensor nuclear norm (TNN) has been widely used in the multi-view subspace clustering problem. It is known that TNN is a convex surrogate of the tensor rank.  ...  ETLMSC: the Markov chain based multi-view subspace clustering via an essential tensor learning method.  ... 
doi:10.1109/access.2021.3085322 fatcat:fk65gxitqbd3tcyrh3eiojwv3q

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis [article]

Guixiang Ma, Chun-Ta Lu, Lifang He, Philip S. Yu, Ann B. Ragin
2017 arXiv   pre-print
However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings.  ...  Multi-view graph embedding has become a widely studied problem in the area of graph learning.  ...  For instance, a multi-modal spectral clustering algorithm is presented in [4] to learn a commonly shared graph Laplacian matrix by unifying different views.  ... 
arXiv:1709.03659v1 fatcat:to7w7jeosfborifktrbt3zhtsu

Special Section Guest Editorial: Representation Learning and Big Data Analytics for Remote Sensing

Weifeng Liu, Yicong Zhou, Karen Panetta, Sos Agaian
2020 Journal of Applied Remote Sensing  
Daizhi Kuang and Juncai Xu combined multiple spectral-spatial features and multikernel support tensor machine for hyperspectral image classification.  ...  These studies indicate that representation learning (e.g. feature extraction) plays an essential role for segmentation, recognition, and classification of remote sensing images.  ...  It is critical to develop comprehensive theories and algorithms for representation learning and big data analytics in remote sensing.  ... 
doi:10.1117/1.jrs.14.032601 fatcat:635nbmh6ojefljv4k3uhxayrmi

Multi-view Hierarchical Clustering [article]

Qinghai Zheng, Jihua Zhu, Shuangxun Ma
2020 arXiv   pre-print
The CDI can explore the underlying complementary information of multi-view data so as to learn an essential distance matrix, which is utilized in NNA to obtain the clustering results.  ...  This paper focuses on the multi-view clustering, which aims to promote clustering results with multi-view data.  ...  For multi-view spectral clustering, the core idea is to learn an intrinsic graph, which contains the information of multi-view data, then get clustering results by performing the spectral clustering algorithm  ... 
arXiv:2010.07573v1 fatcat:errcr2647vaddh5aswjpkqm7ym

A Multi-view Fusion Method via Tensor Learning and Gradient Descent for Image Features

LaiHang Yu, DongYan Zhang, NingZhong Liu, WenGang Zhou
2021 IEEE Access  
Therefore, we propose a multi-view fusion method via tensor learning and gradient descent (MvF-TG) in this paper.  ...  The new method can effectively exploit the spatial correlation information from the multi-view features by tensor learning.  ...  multi-view spectral clustering(Co-trained) [34] .  ... 
doi:10.1109/access.2021.3079499 fatcat:ija4pfmsubdoda7v6vyrstvqsm

Hyper-laplacian Regularized Multi-view Subspace Clustering with a New Weighted Tensor Nuclear Norm

Qingjiang Xiao, Shiqiang Du, Jinmei Song, Yao Yu, Yixuan Huang
2021 IEEE Access  
In this paper, we present a hyper-Laplacian regularized method WHLR-MSC with a new weighted tensor nuclear norm for multi-view subspace clustering.  ...  INDEX TERMS Multi-view subspace clustering, hyper-Laplacian graph, low-rank tensor, weighted tensor nuclear norm.  ...  [26] provide an on unifying multi-view self-representations for clustering by tensor multi-rank minimization (t-SVD-MSC) method, t-SVD-MSC stacks the subspace representation matrices of different views  ... 
doi:10.1109/access.2021.3107673 fatcat:hsi2lykle5atfpgrncwi7qikp4
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