Deep convolutional self-paced clustering
文献类型：期刊论文
| 作者 | Chen, Rui1,2 ; Tang, Yongqiang2; Tian, Lei2,3; Zhang, Caixia1; Zhang, Wensheng2,3
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| 刊名 | APPLIED INTELLIGENCE
 |
| 出版日期 | 2021-07-29 |
| 页码 | 15 |
| 关键词 | Deep clustering Convolutional autoencoder Local structure preservation Self-paced learning |
| ISSN号 | 0924-669X |
| DOI | 10.1007/s10489-021-02569-y |
| 通讯作者 | Tang, Yongqiang(yongqiang.tang@ia.ac.cn) ; Zhang, Caixia(zh_caixia@163.com) |
| 英文摘要 | Clustering is a crucial but challenging task in data mining and machine learning. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, has achieved state-of-the-art performance in various applications and attracted considerable attention. Nevertheless, most of these approaches fail to effectively learn informative cluster-oriented features for data with spatial correlation structure, e.g., images. To tackle this problem, in this paper, we develop a deep convolutional self-paced clustering (DCSPC) method. Specifically, in the pretraining stage, we propose to utilize a convolutional autoencoder to extract a high-quality data representation that contains the spatial correlation information. Then, in the finetuning stage, a clustering loss is directly imposed on the learned features to jointly perform feature refinement and cluster assignment. We retain the decoder to avoid the feature space being distorted by the clustering loss. To stabilize the training process of the whole network, we further introduce a self-paced learning mechanism and select the most confident samples in each iteration. Through comprehensive experiments on seven popular image datasets, we demonstrate that the proposed algorithm can consistently outperform state-of-the-art rivals. |
| WOS关键词 | DIMENSIONALITY |
| 资助项目 | Key-Area Research and Development Program of Guangdong Province[2019B010153002] ; National Natural Science Foundation of China[U1936206] ; National Natural Science Foundation of China[61806202] ; National Natural Science Foundation of China[61803087] ; National Natural Science Foundation of China[61803086] ; Feature Innovation Project of Guangdong Province Department of Education[2019KTSCX192] ; Guangdong Basic and Applied Basic Research Fund[2020B1515310003] ; Foshan Core Technology Research Project[1920001001367] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000679334800002 |
| 出版者 | SPRINGER |
| 资助机构 | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Feature Innovation Project of Guangdong Province Department of Education ; Guangdong Basic and Applied Basic Research Fund ; Foshan Core Technology Research Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45563]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Tang, Yongqiang; Zhang, Caixia |
| 作者单位 | 1.Foshan Univ, Dept Automat, Foshan, Peoples R China 2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Rui,Tang, Yongqiang,Tian, Lei,et al. Deep convolutional self-paced clustering[J]. APPLIED INTELLIGENCE,2021:15. |
| APA | Chen, Rui,Tang, Yongqiang,Tian, Lei,Zhang, Caixia,&Zhang, Wensheng.(2021).Deep convolutional self-paced clustering.APPLIED INTELLIGENCE,15. |
| MLA | Chen, Rui,et al."Deep convolutional self-paced clustering".APPLIED INTELLIGENCE (2021):15. |
入库方式： OAI收割
来源：自动化研究所
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