Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship
文献类型：期刊论文
| 作者 | Liang, Jie1; Yang, Jufeng1; Cheng, Ming-Ming1; Rosin, Paul L.2; Wang, Liang3
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2019-08-01 |
| 卷号 | 28期号:8页码:3973-3985 |
| 关键词 | Subspace clustering triplet relationship estimating the number of clusters hyper-graph clustering |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2019.2903294 |
| 通讯作者 | Yang, Jufeng(yangjufeng@nankai.edu.cn) |
| 英文摘要 | In this paper, we propose a unified framework to discover the number of clusters and group the data points into different clusters using subspace clustering simultaneously. Real data distributed in a high-dimensional space can be disentangled into a union of low-dimensional subspaces, which can benefit various applications. To explore such intrinsic structure, state-of-the-art subspace clustering approaches often optimize a self-representation problem among all samples, to construct a pairwise affinity graph for spectral clustering. However, a graph with pairwise similarities lacks robustness for segmentation, especially for samples which lie on the intersection of two subspaces. To address this problem, we design a hyper-correlation-based data structure termed as the triplet relationship, which reveals high relevance and local compactness among three samples. The triplet relationship can be derived from the self-representation matrix, and be utilized to iteratively assign the data points to clusters. Based on the triplet relationship, we propose a unified optimizing scheme to automatically calculate clustering assignments. Specifically, we optimize a model selection reward and a fusion reward by simultaneously maximizing the similarity of triplets from different clusters while minimizing the correlation of triplets from the same cluster. The proposed algorithm also automatically reveals the number of clusters and fuses groups to avoid over-segmentation. Extensive experimental results on both synthetic and real-world datasets validate the effectiveness and robustness of the proposed method. |
| WOS关键词 | SEGMENTATION ; ALGORITHM ; SELECTION |
| 资助项目 | NSFC[61876094] ; NSFC[61620106008] ; NSFC[61572264] ; Natural Science Foundation of Tianjin, China[18JCYBJC15400] ; Natural Science Foundation of Tianjin, China[18ZXZNGX00110] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Fundamental Research Funds for the Central Universities |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000472609200009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | NSFC ; Natural Science Foundation of Tianjin, China ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/26018]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Yang, Jufeng |
| 作者单位 | 1.Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China 2.Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, S Glam, Wales 3.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liang, Jie,Yang, Jufeng,Cheng, Ming-Ming,et al. Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(8):3973-3985. |
| APA | Liang, Jie,Yang, Jufeng,Cheng, Ming-Ming,Rosin, Paul L.,&Wang, Liang.(2019).Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(8),3973-3985. |
| MLA | Liang, Jie,et al."Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.8(2019):3973-3985. |
入库方式： OAI收割
来源：自动化研究所
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