Multi-View Robust Tensor-Based Subspace Clustering
In this era of technology advancement, huge amount of data is collected from different disciplines. This data needs to be stored, processed and analyzed to understand its nature. Networks or graphs arise to model real-world systems in the different fields. Early work in network theory adopted simple...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9999185/ |
| _version_ | 1797974424442372096 |
|---|---|
| author | Esraa M. Al-Sharoa Mohammad A. Al-Wardat |
| author_facet | Esraa M. Al-Sharoa Mohammad A. Al-Wardat |
| author_sort | Esraa M. Al-Sharoa |
| collection | DOAJ |
| description | In this era of technology advancement, huge amount of data is collected from different disciplines. This data needs to be stored, processed and analyzed to understand its nature. Networks or graphs arise to model real-world systems in the different fields. Early work in network theory adopted simple graphs to model systems where the system’s entities and interactions among them are modeled as nodes and static, single-type edges, respectively. However, this representation is considered limited when the system’s entities interact through different sources. Multi-view networks have recently attracted attention due to its ability to consider the different interactions between entities explicitly. An important tool to understand the structure of multi-view networks is community detection. Community detection or clustering reveals the significant communities in the network which provides dimensionality reduction and a better understanding of the network. In this paper, a new robust clustering algorithm is proposed to detect the community structure in multi-view networks. In particular, the proposed approach constructs a 3-mode tensor from the normalized adjacency matrices that represent the different views. The constructed tensor is decomposed into a self-representation and error components where the extracted self-representation tensor is used to detect the community structure of the multi-view network. Moreover, a common subspace is computed among all views where the contribution of each view to the common subspace is optimized. The proposed method is applied to several real-world data sets and the results show that the proposed method achieves the best performance compared to other state-of-the-art algorithms. |
| first_indexed | 2024-04-11T04:19:43Z |
| format | Article |
| id | doaj.art-9386537407cb415782e6bc77ede1b44c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T04:19:43Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9386537407cb415782e6bc77ede1b44c2022-12-31T00:01:22ZengIEEEIEEE Access2169-35362022-01-011013429213430610.1109/ACCESS.2022.32322859999185Multi-View Robust Tensor-Based Subspace ClusteringEsraa M. Al-Sharoa0https://orcid.org/0000-0001-9648-5046Mohammad A. Al-Wardat1https://orcid.org/0000-0003-3932-2659Department of Electrical Engineering, Jordan University of Science and Technology, Irbid, JordanDepartment of Electrical Engineering, Jordan University of Science and Technology, Irbid, JordanIn this era of technology advancement, huge amount of data is collected from different disciplines. This data needs to be stored, processed and analyzed to understand its nature. Networks or graphs arise to model real-world systems in the different fields. Early work in network theory adopted simple graphs to model systems where the system’s entities and interactions among them are modeled as nodes and static, single-type edges, respectively. However, this representation is considered limited when the system’s entities interact through different sources. Multi-view networks have recently attracted attention due to its ability to consider the different interactions between entities explicitly. An important tool to understand the structure of multi-view networks is community detection. Community detection or clustering reveals the significant communities in the network which provides dimensionality reduction and a better understanding of the network. In this paper, a new robust clustering algorithm is proposed to detect the community structure in multi-view networks. In particular, the proposed approach constructs a 3-mode tensor from the normalized adjacency matrices that represent the different views. The constructed tensor is decomposed into a self-representation and error components where the extracted self-representation tensor is used to detect the community structure of the multi-view network. Moreover, a common subspace is computed among all views where the contribution of each view to the common subspace is optimized. The proposed method is applied to several real-world data sets and the results show that the proposed method achieves the best performance compared to other state-of-the-art algorithms.https://ieeexplore.ieee.org/document/9999185/Multi-view networksoptimizationlow-rank representationtensor decompositionspectral clustering |
| spellingShingle | Esraa M. Al-Sharoa Mohammad A. Al-Wardat Multi-View Robust Tensor-Based Subspace Clustering IEEE Access Multi-view networks optimization low-rank representation tensor decomposition spectral clustering |
| title | Multi-View Robust Tensor-Based Subspace Clustering |
| title_full | Multi-View Robust Tensor-Based Subspace Clustering |
| title_fullStr | Multi-View Robust Tensor-Based Subspace Clustering |
| title_full_unstemmed | Multi-View Robust Tensor-Based Subspace Clustering |
| title_short | Multi-View Robust Tensor-Based Subspace Clustering |
| title_sort | multi view robust tensor based subspace clustering |
| topic | Multi-view networks optimization low-rank representation tensor decomposition spectral clustering |
| url | https://ieeexplore.ieee.org/document/9999185/ |
| work_keys_str_mv | AT esraamalsharoa multiviewrobusttensorbasedsubspaceclustering AT mohammadaalwardat multiviewrobusttensorbasedsubspaceclustering |