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Class-Oriented Self-Learning Graph Embedding for Image Compact Representation

文献类型：期刊论文

作者Hu, Liangchen4; Dai, Zhenlei3; Tian, Lei1,2; Zhang, Wensheng1,2
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2023
卷号33期号:1页码:74-87
关键词Sparse matrices Manifolds Machine learning algorithms Laplace equations Heuristic algorithms Data models Data mining Adaptive graph learning separability examination marginal information preserving L-2,L-p-norm sparsity compact representation
ISSN号1051-8215
DOI10.1109/TCSVT.2022.3197746
通讯作者Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要As one of the learning ways for inducing efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are designed to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within classes, but exclude the affinity of different classes, resulting in inadequate capture of marginal structures between different class distributions. To overcome these issues, we propose a learning framework that implements Class-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can be easily extended to semi-supervised scenarios. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method in comparison to some state-of-the-art approaches.
WOS关键词DIMENSIONALITY REDUCTION ; PRESERVING PROJECTIONS ; FEATURE-SELECTION ; FACE RECOGNITION ; MANIFOLD ; ILLUMINATION ; MODELS
资助项目National Key Research and Development Program of China[2020AAA0109600] ; National Natural Science Foundation of China[62173328] ; National Natural Science Foundation of China[62106266]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000911746000006
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/51335]  
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Zhang, Wensheng
作者单位1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
4.Anhui Normal Univ, Sch Comp & Informat, Wuhu 241002, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Liangchen,Dai, Zhenlei,Tian, Lei,et al. Class-Oriented Self-Learning Graph Embedding for Image Compact Representation[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2023,33(1):74-87.
APA Hu, Liangchen,Dai, Zhenlei,Tian, Lei,&Zhang, Wensheng.(2023).Class-Oriented Self-Learning Graph Embedding for Image Compact Representation.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,33(1),74-87.
MLA Hu, Liangchen,et al."Class-Oriented Self-Learning Graph Embedding for Image Compact Representation".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 33.1(2023):74-87.

入库方式： OAI收割

来源：自动化研究所

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