MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression Article Swipe
Yang Wei
,
Linlin Zhuo
,
Jing Zeng
,
Yijun Zhao
,
Qiaojie Jiang
,
Dongliang Xie
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3052248
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3052248
With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of ℓ2,1 norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of nonuniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm.
Related Topics
Concepts
Cluster analysis
Subspace topology
Computer science
Linear subspace
Tensor (intrinsic definition)
Algorithm
Dimension (graph theory)
Rank (graph theory)
Expression (computer science)
Clustering high-dimensional data
Noise (video)
Data mining
Artificial intelligence
Mathematics
Combinatorics
Programming language
Pure mathematics
Image (mathematics)
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3052248
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09328123.pdf
- OA Status
- gold
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3124158795
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3124158795Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3052248Digital Object Identifier
- Title
-
MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank ExpressionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Yang Wei, Linlin Zhuo, Jing Zeng, Yijun Zhao, Qiaojie Jiang, Dongliang XieList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3052248Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09328123.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09328123.pdfDirect OA link when available
- Concepts
-
Cluster analysis, Subspace topology, Computer science, Linear subspace, Tensor (intrinsic definition), Algorithm, Dimension (graph theory), Rank (graph theory), Expression (computer science), Clustering high-dimensional data, Noise (video), Data mining, Artificial intelligence, Mathematics, Combinatorics, Programming language, Pure mathematics, Image (mathematics), GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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