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Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces

文献类型：期刊论文

作者Zhang, Junping1,2; Wang, Xiaodan1,2; Kruger, Uwe3; Wang, Fei-Yue4,5
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS
出版日期2011-03-01
卷号22期号:3页码:367-380
关键词Manifold learning principal component analysis principal curves space partitioning tree-based algorithms
英文摘要Most partitioning algorithms iteratively partition a space into cells that contain underlying linear or nonlinear structures using linear partitioning strategies. The compactness of each cell depends on how well the (locally) linear partitioning strategy approximates the intrinsic structure. To partition a compact structure for complex data in a nonlinear context, this paper proposes a nonlinear partition strategy. This is a principal curve tree (PC-tree), which is implemented iteratively. Given that a PC passes through the middle of the data distribution, it allows for partitioning based on the arc length of the PC. To enhance the partitioning of a given space, a residual version of the PC-tree algorithm is developed, denoted here as the principal component analysis tree (PCR-tree) algorithm. Because of its residual property, the PCR-tree can yield the intrinsic dimension of high-dimensional data. Comparisons presented in this paper confirm that the proposed PC-tree and PCR-tree approaches show a better performance than several other competing partitioning algorithms in terms of vector quantization error and nearest neighbor search. The comparison also shows that the proposed algorithms outperform competing linear methods in total average coverage which measures the nonlinear compactness of partitioning algorithms.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]REDUCTION ; MANIFOLDS ; SURFACES ; MODEL
收录类别SCI
语种英语
WOS记录号WOS:000287862500004
源URL[http://ir.ia.ac.cn/handle/173211/3578]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
作者单位1.Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China
2.Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China
3.Petr Inst, Dept Chem Engn, Abu Dhabi 2533, U Arab Emirates
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
5.Univ Arizona, Tucson, AZ 85719 USA
推荐引用方式
GB/T 7714		 Zhang, Junping,Wang, Xiaodan,Kruger, Uwe,et al. Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2011,22(3):367-380.
APA Zhang, Junping,Wang, Xiaodan,Kruger, Uwe,&Wang, Fei-Yue.(2011).Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces.IEEE TRANSACTIONS ON NEURAL NETWORKS,22(3),367-380.
MLA Zhang, Junping,et al."Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces".IEEE TRANSACTIONS ON NEURAL NETWORKS 22.3(2011):367-380.

入库方式： OAI收割

来源：自动化研究所

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