Prime Discriminant Simplicial Complex
Zhang, Junping1; Xie, Ziyu1; Li, Stan Z.2
2013
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号24期号:1页码:133-144
文章类型Article
摘要The structure representation of data distribution plays an important role in understanding the underlying mechanism of generating data. In this paper, we propose the prime discriminant simplicial complex (PDSC) by utilizing persistent homology to capture such structures. Assuming that each class is represented with a prime simplicial complex, we classify unlabeled samples based on the nearest projection distances from the samples to the simplicial complexes. We also extend the extrapolation ability of these complexes with a projection constraint term. Experiments in simulated and practical datasets indicate that, compared with several published algorithms, the proposed PDSC approaches achieve promising performance without losing structure representation.
关键词Object Recognition Persistent Homology Supervised Learning Topology
WOS标题词Science & Technology ; Technology
关键词[WOS]NONLINEAR DIMENSIONALITY REDUCTION ; DATA VISUALIZATION ; ALPHA-SHAPES ; NETWORKS ; MAPS
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000312844800012
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/7947
专题模式识别国家重点实验室_生物识别与安全技术研究
作者单位1.Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
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Zhang, Junping,Xie, Ziyu,Li, Stan Z.. Prime Discriminant Simplicial Complex[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2013,24(1):133-144.
APA Zhang, Junping,Xie, Ziyu,&Li, Stan Z..(2013).Prime Discriminant Simplicial Complex.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,24(1),133-144.
MLA Zhang, Junping,et al."Prime Discriminant Simplicial Complex".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 24.1(2013):133-144.
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