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Nonlinear dimensionality reduction
Known as:
Non-linear dimensionality reduction
, Locally linear embeddings
, Locally linear embedding
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High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to…
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Related topics
Related topics
42 relations
Autoencoder
Backpropagation
Curse of dimensionality
Degree matrix
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
Multi-feature multi-manifold learning for single-sample face recognition
Haibin Yan
,
Jiwen Lu
,
Xiuzhuang Zhou
,
Yuanyuan Shang
Neurocomputing
2014
Corpus ID: 30919403
2010
2010
Event monitoring via local motion abnormality detection in non-linear subspace
Ioannis Tziakos
,
A. Cavallaro
,
Li-Qun Xu
Neurocomputing
2010
Corpus ID: 8761363
Highly Cited
2009
Highly Cited
2009
Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
Jiawei Han
,
Deng Cai
2009
Corpus ID: 118093129
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use…
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2009
2009
Outlier exploration and diagnostic classification of a multi-centre 1H-MRS brain tumour database
A. Vellido
,
E. Romero
,
F. F. González-Navarro
,
L. B. Muñoz
,
M. Julià-Sapé
,
C. Arús
Neurocomputing
2009
Corpus ID: 207101145
2009
2009
Rank transformation and manifold learning for multivariate mathematical morphology
O. Lézoray
,
C. Charrier
,
A. Elmoataz
European Signal Processing Conference
2009
Corpus ID: 6632894
The extension of lattice based operators to multivariate images is still a challenging theme in mathematical morphology. In this…
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2007
2007
Diffusion Maps and Geometric Harmonics for Automatic Target Recognition (ATR). Volume 2. Appendices
S. Zucker
,
R. Coifman
2007
Corpus ID: 33721827
Abstract : Geometric harmonics provides a framework for taking data in high-dimensional measurement spaces and embedding them in…
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2007
2007
A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding
Xianhua Zeng
,
Siwei Luo
International Conference on Advanced Data Mining…
2007
Corpus ID: 27398666
Neighborhood Preserving Embedding (NPE) is an unsupervised manifold learning algorithm with subspace learning characteristic. In…
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Highly Cited
2006
Highly Cited
2006
Modeling Correspondences for Multi-Camera Tracking Using Nonlinear Manifold Learning and Target Dynamics
Vlad I. Morariu
,
O. Camps
Computer Vision and Pattern Recognition
2006
Corpus ID: 4664124
Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D…
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2006
2006
Incremental Construction of Neighborhood Graphs for Nonlinear Dimensionality Reduction
Dongfang Zhao
,
Li Yang
International Conference on Pattern Recognition
2006
Corpus ID: 14940460
Most nonlinear data embedding methods use bottom-up approaches for capturing underlying structures of data distributed as points…
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Highly Cited
2002
Highly Cited
2002
Efficient Simplicial Reconstructions of Manifolds from Their Samples
D. Freedman
IEEE Transactions on Pattern Analysis and Machine…
2002
Corpus ID: 9421114
An algorithm for manifold learning is presented. Given only samples of a finite-dimensional differentiable manifold and no a…
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