CASIA OpenIR
Deep Self-Evolution Clustering
Chang, Jianlong1,2; Meng, Gaofeng1; Wang, Lingfeng1; Xiang, Shiming1,2; Pan, Chunhong1
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2020-04-01
Volume42Issue:4Pages:809-823
Abstract

Clustering is a crucial but challenging task in pattern analysis and machine learning. Existing methods often ignore the combination between representation learning and clustering. To tackle this problem, we reconsider the clustering task from its definition to develop Deep Self-Evolution Clustering (DSEC) to jointly learn representations and cluster data. For this purpose, the clustering task is recast as a binary pairwise-classification problem to estimate whether pairwise patterns are similar. Specifically, similarities between pairwise patterns are defined by the dot product between indicator features which are generated by a deep neural network (DNN). To learn informative representations for clustering, clustering constraints are imposed on the indicator features to represent specific concepts with specific representations. Since the ground-truth similarities are unavailable in clustering, an alternating iterative algorithm called Self-Evolution Clustering Training (SECT) is presented to select similar and dissimilar pairwise patterns and to train the DNN alternately. Consequently, the indicator features tend to be one-hot vectors and the patterns can be clustered by locating the largest response of the learned indicator features. Extensive experiments strongly evidence that DSEC outperforms current models on twelve popular image, text and audio datasets consistently.

KeywordTask analysis Unsupervised learning Training Clustering methods Pattern analysis Clustering deep self-evolution clustering self-evolution clustering training deep unsupervised learning
DOI10.1109/TPAMI.2018.2889949
WOS KeywordIMAGE RETRIEVAL ; REPRESENTATIONS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[61573352] ; Beijing Natural Science Foundation[L172053]
Funding OrganizationNational Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000526541100004
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38863
Collection中国科学院自动化研究所
Corresponding AuthorWang, Lingfeng
Affiliation1.Chinese Acad Sci, Inst Automat, Dept Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chang, Jianlong,Meng, Gaofeng,Wang, Lingfeng,et al. Deep Self-Evolution Clustering[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2020,42(4):809-823.
APA Chang, Jianlong,Meng, Gaofeng,Wang, Lingfeng,Xiang, Shiming,&Pan, Chunhong.(2020).Deep Self-Evolution Clustering.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,42(4),809-823.
MLA Chang, Jianlong,et al."Deep Self-Evolution Clustering".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 42.4(2020):809-823.
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