Nonlinear Dimension Reduction by Local Multidimensional Scaling

Frontiers in Algorithmics - Lecture Notes in Computer Science ◽

10.1007/978-3-319-39817-4_16 ◽

2016 ◽

pp. 158-171

Author(s):

Yuzhe Ma ◽

Kun He ◽

John Hopcroft ◽

Pan Shi

Keyword(s):

Multidimensional Scaling ◽

Dimension Reduction ◽

Nonlinear Dimension Reduction ◽

Nonlinear Dimension

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Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis

Journal of the American Statistical Association ◽

10.1198/jasa.2009.0111 ◽

2009 ◽

Vol 104 (485) ◽

pp. 209-219 ◽

Cited By ~ 75

Author(s):

Lisha Chen ◽

Andreas Buja

Keyword(s):

Multidimensional Scaling ◽

Dimension Reduction ◽

Graph Drawing ◽

Proximity Analysis ◽

Nonlinear Dimension Reduction ◽

Nonlinear Dimension ◽

Reduction Graph

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Nonlinear dimension reduction for conditional quantiles

Advances in Data Analysis and Classification ◽

10.1007/s11634-021-00439-6 ◽

2021 ◽

Author(s):

Eliana Christou ◽

Annabel Settle ◽

Andreas Artemiou

Keyword(s):

Dimension Reduction ◽

Conditional Quantiles ◽

Nonlinear Dimension Reduction ◽

Nonlinear Dimension

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A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Big Data

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2018.2876848 ◽

2019 ◽

Vol 31 (12) ◽

pp. 2249-2261 ◽

Cited By ~ 1

Author(s):

R. Krishnan ◽

V. A. Samaranayake ◽

S. Jagannathan

Keyword(s):

Big Data ◽

Dimension Reduction ◽

Nonlinear Dimension Reduction ◽

Nonlinear Dimension ◽

Reduction Approach

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Nonlinear Dimension Reduction via Local Tangent Space Alignment

Intelligent Data Engineering and Automated Learning - Lecture Notes in Computer Science ◽

10.1007/978-3-540-45080-1_66 ◽

2003 ◽

pp. 477-481 ◽

Cited By ~ 30

Author(s):

Zhenyue Zhang ◽

Hongyuan Zha

Keyword(s):

Dimension Reduction ◽

Tangent Space ◽

Nonlinear Dimension Reduction ◽

Nonlinear Dimension ◽

Local Tangent

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Nonlinear dimension reduction with Wright-Fisher kernel for genotype aggregation and association mapping

Bioinformatics ◽

10.1093/bioinformatics/bts406 ◽

2012 ◽

Vol 28 (18) ◽

pp. i375-i381 ◽

Cited By ~ 3

Author(s):

H. Zhu ◽

L. Li ◽

H. Zhou

Keyword(s):

Association Mapping ◽

Dimension Reduction ◽

Fisher Kernel ◽

Nonlinear Dimension Reduction ◽

Nonlinear Dimension

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Nonlinear dimension reduction using ISOMap based on class information

2009 International Joint Conference on Neural Networks ◽

10.1109/ijcnn.2009.5178988 ◽

2009 ◽

Cited By ~ 1

Author(s):

Minkook Cho ◽

Hyeyoung Park

Keyword(s):

Dimension Reduction ◽

Nonlinear Dimension Reduction ◽

Class Information ◽

Nonlinear Dimension

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Nonlinear dimension reduction via outer Bi-Lipschitz extensions

Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing - STOC 2018 ◽

10.1145/3188745.3188828 ◽

2018 ◽

Cited By ~ 2

Author(s):

Sepideh Mahabadi ◽

Konstantin Makarychev ◽

Yury Makarychev ◽

Ilya Razenshteyn

Keyword(s):

Dimension Reduction ◽

Nonlinear Dimension Reduction ◽

Lipschitz Extensions ◽

Nonlinear Dimension

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Nonlinear Dimension Reduction and Activation Detection for fMRI Dataset

2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06) ◽

10.1109/cvprw.2006.144 ◽

2006 ◽

Author(s):

Xilin Shen ◽

F.G. Meyer

Keyword(s):

Dimension Reduction ◽

Nonlinear Dimension Reduction ◽

Activation Detection ◽

Nonlinear Dimension

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Nonlinear Signal Sources Estimation Based on Nonlinear Dimension Reduction

DEStech Transactions on Engineering and Technology Research ◽

10.12783/dtetr/apop2017/18734 ◽

2018 ◽

Author(s):

Hong-yi LI ◽

Zhi-xuan MA ◽

Di ZHAO

Keyword(s):

Dimension Reduction ◽

Nonlinear Dimension Reduction ◽

Nonlinear Dimension ◽

Nonlinear Signal ◽

Signal Sources

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Neural Network for Nonlinear Dimension Reduction Through Manifold Recovery

10.1109/urtc49097.2019.9660541 ◽

2019 ◽

Author(s):

Jessica Bader ◽

Declan Nelson ◽

Thalia Chai-Zhang ◽

Walter Gerych ◽

Elke Rundensteiner

Keyword(s):

Neural Network ◽

Dimension Reduction ◽

Nonlinear Dimension Reduction ◽

Nonlinear Dimension

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