Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces

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	Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces
	Zhang, Junping 1,2; Wang, Xiaodan 1,2; Kruger, Uwe 3; Wang, Fei-Yue4,5
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS
	2011-03-01
卷号	22 期号:3 页码:367-380
文章类型	Article
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.
关键词	Manifold Learning Principal Component Analysis Principal Curves Space Partitioning Tree-based Algorithms
WOS标题词	Science & Technology ; Technology
关键词[WOS]	REDUCTION ; MANIFOLDS ; SURFACES ; MODEL
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:000287862500004
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3578
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
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.