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Maximal Linear Embedding for Dimensionality Reduction

文献类型：期刊论文

作者Wang, Ruiping1; Shan, Shiguang2; Chen, Xilin2; Chen, Jie3; Gao, Wen4
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2011-09-01
卷号33期号:9页码:1776-1792
关键词Dimensionality reduction manifold learning maximal linear patch landmarks-based global alignment
ISSN号0162-8828
DOI10.1109/TPAMI.2011.39
英文摘要Over the past few decades, dimensionality reduction has been widely exploited in computer vision and pattern analysis. This paper proposes a simple but effective nonlinear dimensionality reduction algorithm, named Maximal Linear Embedding (MLE). MLE learns a parametric mapping to recover a single global low-dimensional coordinate space and yields an isometric embedding for the manifold. Inspired by geometric intuition, we introduce a reasonable definition of locally linear patch, Maximal Linear Patch (MLP), which seeks to maximize the local neighborhood in which linearity holds. The input data are first decomposed into a collection of local linear models, each depicting an MLP. These local models are then aligned into a global coordinate space, which is achieved by applying MDS to some randomly selected landmarks. The proposed alignment method, called Landmarks-based Global Alignment (LGA), can efficiently produce a closed-form solution with no risk of local optima. It just involves some small-scale eigenvalue problems, while most previous aligning techniques employ time-consuming iterative optimization. Compared with traditional methods such as ISOMAP and LLE, our MLE yields an explicit modeling of the intrinsic variation modes of the observation data. Extensive experiments on both synthetic and real data indicate the effectivity and efficiency of the proposed algorithm.
资助项目Natural Science Foundation of China[61025010] ; Natural Science Foundation of China[60872077] ; National Basic Research Program of China (973 Program)[2009CB320902]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000292740000006
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/13075]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Ruiping
作者单位1.Tsinghua Univ, Dept Automat, Broadband Network & Multimedia Lab, Beijing 100084, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Univ Oulu, Dept Elect & Informat Engn, Machine Vis Grp, FI-90014 Oulu, Finland
4.Peking Univ, Sch EECS, Key Lab Machine Percept MoE, Beijing 100871, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Ruiping,Shan, Shiguang,Chen, Xilin,et al. Maximal Linear Embedding for Dimensionality Reduction[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2011,33(9):1776-1792.
APA Wang, Ruiping,Shan, Shiguang,Chen, Xilin,Chen, Jie,&Gao, Wen.(2011).Maximal Linear Embedding for Dimensionality Reduction.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,33(9),1776-1792.
MLA Wang, Ruiping,et al."Maximal Linear Embedding for Dimensionality Reduction".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 33.9(2011):1776-1792.

入库方式： OAI收割

来源：计算技术研究所

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