Deep self-supervised clustering with embedding adjacent graph features
Deep clustering uses neural networks to learn the low-dimensional feature representations suitable for clustering tasks. Numerous studies have shown that learning embedded features and defining the clustering loss properly contribute to better performance. However, most of the existing studies focus...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2022.2048321 |
| _version_ | 1828404336935305216 |
|---|---|
| author | Xiao Jiang Pengjiang Qian Yizhang Jiang Yi Gu Aiguo Chen |
| author_facet | Xiao Jiang Pengjiang Qian Yizhang Jiang Yi Gu Aiguo Chen |
| author_sort | Xiao Jiang |
| collection | DOAJ |
| description | Deep clustering uses neural networks to learn the low-dimensional feature representations suitable for clustering tasks. Numerous studies have shown that learning embedded features and defining the clustering loss properly contribute to better performance. However, most of the existing studies focus on the deep local features and ignore the global spatial characteristics of the original data space. To address this issue, this paper proposes deep self-supervised clustering with embedding adjacent graph features (DSSC-EAGF). The significance of our efforts is three-fold: 1) To obtain the deep representation of the potential global spatial structure, a dedicated adjacent graph matrix is designed and used to train the autoencoder in the original data space; 2) In the deep encoding feature space, the KNN algorithm is used to obtain the virtual clusters for devising a self-supervised learning loss. Then, the reconstruction loss, clustering loss, and self-supervised loss are integrated, and a novel overall loss measurement is proposed for DSSC-EAGF. 3) An inverse-Y-shaped network model is designed to well learn the features of both the local and the global structures of the original data, which greatly improves the clustering performance. The experimental studies prove the superiority of the proposed DSSC-EAGF against a few state-of-the-art deep clustering methods. |
| first_indexed | 2024-12-10T10:34:36Z |
| format | Article |
| id | doaj.art-2b45cb55195340a2946e028ca7691d9b |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-12-10T10:34:36Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-2b45cb55195340a2946e028ca7691d9b2022-12-22T01:52:28ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832022-12-0110133634610.1080/21642583.2022.2048321Deep self-supervised clustering with embedding adjacent graph featuresXiao Jiang0Pengjiang Qian1Yizhang Jiang2Yi Gu3Aiguo Chen4School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, People’s Republic of ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, People’s Republic of ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, People’s Republic of ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, People’s Republic of ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, People’s Republic of ChinaDeep clustering uses neural networks to learn the low-dimensional feature representations suitable for clustering tasks. Numerous studies have shown that learning embedded features and defining the clustering loss properly contribute to better performance. However, most of the existing studies focus on the deep local features and ignore the global spatial characteristics of the original data space. To address this issue, this paper proposes deep self-supervised clustering with embedding adjacent graph features (DSSC-EAGF). The significance of our efforts is three-fold: 1) To obtain the deep representation of the potential global spatial structure, a dedicated adjacent graph matrix is designed and used to train the autoencoder in the original data space; 2) In the deep encoding feature space, the KNN algorithm is used to obtain the virtual clusters for devising a self-supervised learning loss. Then, the reconstruction loss, clustering loss, and self-supervised loss are integrated, and a novel overall loss measurement is proposed for DSSC-EAGF. 3) An inverse-Y-shaped network model is designed to well learn the features of both the local and the global structures of the original data, which greatly improves the clustering performance. The experimental studies prove the superiority of the proposed DSSC-EAGF against a few state-of-the-art deep clustering methods.https://www.tandfonline.com/doi/10.1080/21642583.2022.2048321Deep clusteringadjacent graph featuresself-supervised learning |
| spellingShingle | Xiao Jiang Pengjiang Qian Yizhang Jiang Yi Gu Aiguo Chen Deep self-supervised clustering with embedding adjacent graph features Systems Science & Control Engineering Deep clustering adjacent graph features self-supervised learning |
| title | Deep self-supervised clustering with embedding adjacent graph features |
| title_full | Deep self-supervised clustering with embedding adjacent graph features |
| title_fullStr | Deep self-supervised clustering with embedding adjacent graph features |
| title_full_unstemmed | Deep self-supervised clustering with embedding adjacent graph features |
| title_short | Deep self-supervised clustering with embedding adjacent graph features |
| title_sort | deep self supervised clustering with embedding adjacent graph features |
| topic | Deep clustering adjacent graph features self-supervised learning |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2022.2048321 |
| work_keys_str_mv | AT xiaojiang deepselfsupervisedclusteringwithembeddingadjacentgraphfeatures AT pengjiangqian deepselfsupervisedclusteringwithembeddingadjacentgraphfeatures AT yizhangjiang deepselfsupervisedclusteringwithembeddingadjacentgraphfeatures AT yigu deepselfsupervisedclusteringwithembeddingadjacentgraphfeatures AT aiguochen deepselfsupervisedclusteringwithembeddingadjacentgraphfeatures |