Skip to search formSkip to main contentSkip to account menu

Nonlinear dimensionality reduction

Known as: Non-linear dimensionality reduction, Locally linear embeddings, Locally linear embedding 
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to… 
Wikipedia (opens in a new tab)

Related topics

Related topics

42 relations
AutoencoderBackpropagationCurse of dimensionalityDegree matrix

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
2010
2010
Event monitoring via local motion abnormality detection in non-linear subspace
Highly Cited
2009
Highly Cited
2009
Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use… 
2009
2009
Outlier exploration and diagnostic classification of a multi-centre 1H-MRS brain tumour database
2008
2008
Person-Independent Head Pose Estimation Using Biased Manifold Embedding
Head pose estimation has been an integral problem in the study of face recognition systems and human-computer interfaces, as part… 
2007
2007
Diffusion Maps and Geometric Harmonics for Automatic Target Recognition (ATR). Volume 2. Appendices
Abstract : Geometric harmonics provides a framework for taking data in high-dimensional measurement spaces and embedding them in… 
Highly Cited
2006
Highly Cited
2006
Modeling Correspondences for Multi-Camera Tracking Using Nonlinear Manifold Learning and Target Dynamics
Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D… 
2005
2005
Local Multidimensional Scaling with Controlled Tradeoff Between Trustworthiness and Continuity
In a visualization task, every nonlinear projection method needs to make a compromise between trustworthiness and continuity. In… 
Highly Cited
2005
Highly Cited
2005
Computer Vision for Biomedical Image Applications, First International Workshop, CVBIA 2005, Beijing, China, October 21, 2005, Proceedings
Highly Cited
2002
Highly Cited
2002
Efficient Simplicial Reconstructions of Manifolds from Their Samples
An algorithm for manifold learning is presented. Given only samples of a finite-dimensional differentiable manifold and no a… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)