Collaborative multi-view K-means clustering
Sign Up to like & getrecommendations! Published in 2019 at "Soft Computing"
DOI: 10.1007/s00500-017-2801-6
Abstract: Due to the huge diversity and heterogeneity of data coming from websites and new technologies, data contents can be better represented by multiple representations for taking advantage of their complementary characteristics efficiently. This paper presents… read more here.
Keywords: collaborative multi; view clustering; view; multi view ... See more keywords
Self-paced and auto-weighted multi-view clustering
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DOI: 10.1016/j.neucom.2019.11.104
Abstract: Abstract Multi-view clustering (MVC) methods are effective approaches to enhance clustering performance by exploiting complementary information from multiple views. One main disadvantage of most existing MVC methods is that the corresponding optimization problems are non-convex… read more here.
Keywords: view clustering; view; multi view; auto weighted ... See more keywords
An overview of recent multi-view clustering
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DOI: 10.1016/j.neucom.2020.02.104
Abstract: Abstract With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional data that describes objects from single perspective, multi-view data is semantically richer, more… read more here.
Keywords: view clustering; overview recent; view; clustering algorithms ... See more keywords
A view of clustering as emergent and innovative processes
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DOI: 10.1080/13662716.2020.1718618
Abstract: ABSTRACT The aim of government cluster programmes is to create clusters that strengthen businesses competitive edge and generate local development. Recent research, however, identifies missing elements regarding agency in existing path dependency explanations of the… read more here.
Keywords: view clustering; emergent innovative; innovative processes; clustering emergent ... See more keywords
Multi-View Clustering Based on Multiple Manifold Regularized Non-Negative Sparse Matrix Factorization
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DOI: 10.1109/access.2022.3216705
Abstract: Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and… read more here.
Keywords: view data; matrix; view; non negative ... See more keywords
Tensorial Multi-Linear Multi-View Clustering via Schatten-p Norm
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DOI: 10.1109/access.2023.3241810
Abstract: Despite satisfactory clustering performance, current subspace-based multi-view clustering methods still suffer from the following limitations. 1) They usually concentrate on the data features in linear subspaces and ignore the features in nonlinear subspaces. 2) They… read more here.
Keywords: tensorial multi; schatten norm; multi; multi view ... See more keywords
Diversity Multi-View Clustering With Subspace and NMF-Based Manifold Learning
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DOI: 10.1109/access.2023.3264837
Abstract: Since the complementarity information among multiple views has been exploited to improve the clustering effect significantly, multi-view clustering has become a hot topic, and many multi-view clustering methods have emerged. Most of them only consider… read more here.
Keywords: diversity multi; manifold learning; multi view; view clustering ... See more keywords
Tensorial Multi-View Clustering via Low-Rank Constrained High-Order Graph Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2022.3143848
Abstract: Multi-view clustering aims to partition multi-view data into different categories by optimally exploring the consistency and complementary information from multiple sources. However, most existing multi-view clustering algorithms heavily rely on the similarity graphs from respective… read more here.
Keywords: view; high order; multi view; view clustering ... See more keywords
Knowledge Graph Embedding Based on Multi-View Clustering Framework
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DOI: 10.1109/tkde.2019.2931548
Abstract: Knowledge representation is one of the critical problems in knowledge engineering and artificial intelligence, while knowledge embedding as a knowledge representation methodology indicates entities and relations in knowledge graph as low-dimensional, continuous vectors. In this… read more here.
Keywords: knowledge graph; view clustering; multi view; based multi ... See more keywords
Incomplete Multi-View Clustering With Reconstructed Views
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DOI: 10.1109/tkde.2021.3112114
Abstract: As one category of important incomplete multi-view clustering methods, subspace based methods seek the common latent representation of incomplete multi-view data by matrix factorization and then partition the latent representation to get clustering results. However,… read more here.
Keywords: reconstructed views; multi view; incomplete multi; view clustering ... See more keywords
Incomplete Multi-View Clustering With Sample-Level Auto-Weighted Graph Fusion
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DOI: 10.1109/tkde.2022.3171911
Abstract: Incomplete multi-view clustering (IMC) has received considerable attention due to its flexibility in fusing the multi-view information when the view samples are partly missing. However, existing methods seldom consider the affection of the missing samples… read more here.
Keywords: view; sample level; multi view; graph fusion ... See more keywords