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Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship
Liang, Jie1; Yang, Jufeng1; Cheng, Ming-Ming1; Rosin, Paul L.2; Wang, Liang3
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-08-01
Volume28Issue:8Pages:3973-3985
Corresponding AuthorYang, Jufeng(yangjufeng@nankai.edu.cn)
AbstractIn 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.
KeywordSubspace clustering triplet relationship estimating the number of clusters hyper-graph clustering
DOI10.1109/TIP.2019.2903294
WOS KeywordSEGMENTATION ; ALGORITHM ; SELECTION
Indexed BySCI
Language英语
Funding ProjectNSFC[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
Funding OrganizationNSFC ; Natural Science Foundation of Tianjin, China ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Fundamental Research Funds for the Central Universities
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000472609200009
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26019
Collection智能感知与计算研究中心
Corresponding AuthorYang, Jufeng
Affiliation1.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
Recommended Citation
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.
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