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Multi-view clustering ensembles
2013
2013 International Conference on Machine Learning and Cybernetics
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Experimental results show the good performance of multi-view spectral clustering ensembles and multiview kernel k-means clustering ensembles on real datasets. ...
In this paper, we proposed multi-view clustering ensembles which extend clustering ensembles to multiview clustering. ...
We described multiview kernel k-means clustering ensembles (MvKKMCE) and multi-view spectral clustering ensembles (MvSpecCE) in Algorithm 1 and Algorithm 2 respectively. ...
doi:10.1109/icmlc.2013.6890443
dblp:conf/icmlc/XieS13
fatcat:oj6677q25zhxzc4md3q4mbp7r4
Dynamic CR-based Classifiers Ensemble based on Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification
2022
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In this article, two CR-based dynamic ensemble selection (DES) methods using multiview kernel collaborative subspace clustering (MVKCSC) and random subspace MVKCSC (RSMVKCSC) are proposed. ...
In order to combine spectral and spatial information to construct a region of competence (RoC), the multiview learning strategy is used in the general DES method. ...
Multiview clustering (MVC) considers spectral information and other spatial information simultaneously to obtain complementary clustering results. ...
doi:10.1109/jstars.2022.3158761
fatcat:nopnupirovd4ji6canfoaaro2m
From Ensemble Clustering to Multi-View Clustering
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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Moreover, the spectral ensemble clustering task is also involved by our framework with a carefully designed constraint, making MVEC a unified optimization framework to achieve the final consensus partition ...
To overcome this problem, we propose a novel Multi-View Ensemble Clustering (MVEC) framework to solve MVC in an Ensemble Clustering (EC) way, which generates Basic Partitions (BPs) for each view individually ...
, and multi-view clustering methods (e.g., Spectral SUM , CRSC and RMVC) that directly use the multiview data. ...
doi:10.24963/ijcai.2017/396
dblp:conf/ijcai/TaoLLDF17
fatcat:erkqo3ketjc3tbr77gzbmffpma
Rényi divergence minimization based co-regularized multiview clustering
2016
Machine Learning
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An existing method of probabilistic multiview clustering is recovered as a special case of the proposed method. ...
Multiview clustering is a framework for grouping objects given multiple views, e.g. text and image views describing the same set of entities. ...
-Co-regularized Spectral Clustering (Co-reg (Sp)) (Kumar et al. 2011): This is the state-ofthe-art spectral multiview clustering. ...
doi:10.1007/s10994-016-5543-2
fatcat:grnmllf3wrbbfeac3nzl2yny4m
Consistency Enhancement-Based Deep Multiview Clustering via Contrastive Learning
[article]
2024
arXiv
pre-print
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2024 pre-print arXiv:2401.12648v1 fatcat:5ddbq3t5ivdyhnm7hclxym7xl4Other Versions
Multiview clustering (MVC) segregates data samples into meaningful clusters by synthesizing information across multiple views. ...
Furthermore, the representation process for clustering is enhanced through spectral clustering, and the consistency across multiple views is improved. ...
Spectral Clustering Spectral clustering is rooted in graph theory and uses graph representations of data to construct a clustering structure. ...
arXiv:2401.12648v3
fatcat:7uwgpfqdgjdxzdn3ccesbrhiba
Multiview Partitioning via Tensor Methods
2013
IEEE Transactions on Knowledge and Data Engineering
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Index Terms-Multi-view clustering, tensor decomposition, spectral clustering, multi-linear singular value decomposition, higher-order orthogonal iteration X. Liu is with the ...
In this paper, we present a novel tensor-based framework for integrating heterogeneous multi-view data in the context of spectral clustering. ...
A multiview clustering strategy via canonical correlation analysis (CCA) is presented in [5] . This method assumes that the views are uncorrelated given the cluster label. ...
doi:10.1109/tkde.2012.95
fatcat:c3fzmbheh5fcphw33lciyyjavm
Adaptive Anchor-Based Partial Multiview Clustering
2020
IEEE Access
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SPECTRAL CLUSTERING After obtaining the fused similarity matrix S, we perform spectral clustering to obtain the final clustering result. ...
Finally, perform spectral clustering on the consensus matrix to obtain clustering results. ...
doi:10.1109/access.2020.3025881
fatcat:q2pdcdcpf5bbrev2nkzakijll4
A Survey on Multi-View Clustering
[article]
2018
arXiv
pre-print
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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. ...
multiview kernel k-means and multi-view spectral clustering [41] . ...
arXiv:1712.06246v2
fatcat:w3b2hfnqyzbbbfcz6t3gl5mlny
Multiview learning for understanding functional multiomics
2020
PLoS Computational Biology
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Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple ...
Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). ...
As spectral clustering has been proven to be equivalent to NMF [8] , multiview spectral clustering is also related to multiview NMF. ...
doi:10.1371/journal.pcbi.1007677
pmid:32240163
pmcid:PMC7117667
fatcat:jqpizdutnrgtnlymfmjhh4qo4q
Large-Scale Spectral Clustering Based on Representative Points
2019
Mathematical Problems in Engineering
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In order to achieve fast spectral clustering, we propose a novel approach, called representative point-based spectral clustering (RPSC), to efficiently deal with the large-scale spectral clustering problem ...
However, most traditional spectral clustering methods still face challenges in the successful application of large-scale spectral clustering problems mainly due to their high computational complexity οn3 ...
a superior multiview spectral clustering consensus [14] . ...
doi:10.1155/2019/5864020
fatcat:zvd737rgo5b3jidxpnoqbts3i4
Multi-view clustering: A survey
2018
Big Data Mining and Analytics
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namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. ...
Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. ...
[84] studied convex sparse spectral clustering with sparse regularization for single view data, and proposed a pairwise sparse spectral clustering for handling multiview data. ...
doi:10.26599/bdma.2018.9020003
dblp:journals/bigdatama/YangW18
fatcat:jxfs7s5b2ndi3lyappfttchoim
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity
[article]
2023
arXiv
pre-print
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In light of this, we propose a fast multi-view clustering via ensembles (FastMICE) approach. ...
Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage ...
Especially, the graph partitioning via spectral clustering is adopted as an important step in many MVC algorithms, such as multiview spectral clustering [3] , [8] , multi-view subspace clustering [6 ...
arXiv:2203.11572v2
fatcat:r6kjqidk3vbcjbufnwvctapvoy
Large-Scale Semantic 3D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest - Part A
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Terms-Classification, convolutional neural network (CNN), Data Fusion Contest (DFC), deep learning, elevation model, height estimation, image analysis and data fusion (IADF), light detection and ranging (LiDAR), multiview ...
We compute height clusters for every pixel in the final DSM and select the mean height of the cluster with the highest number of points. ...
The top-down pathways were constructed via successive up-sampling. ...
doi:10.1109/jstars.2020.3032221
fatcat:2j4er43i7bbs5n23bhad5fnyca
New Approaches in Multi-View Clustering
[chapter]
2018
Recent Applications in Data Clustering
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learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning. ...
Due to this, multiview learning has drawn much attention from both academia and industry. Compared to single-view learning, multi-view learning has demonstrated plenty of advantages. ...
Multi-view clustering via spectral clustering Spectral clustering is built upon the spectral graph theory. ...
doi:10.5772/intechopen.75598
fatcat:jniifuf4ync27fofz4fpbnfiia
Low-rank Multi-view Clustering in Third-Order Tensor Space
[article]
2016
arXiv
pre-print
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., clustering, classification, de-noising. Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace. ...
As a consequence, the clustering performance for multi-view data is compromised. ...
Base on the learned representation, the spectral clustering via Markov chain is applied to final separation subsequently. ...
arXiv:1608.08336v2
fatcat:o6nfpspgivfrljyif3aocau74a
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