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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… 
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Related topics

Related topics

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AutoencoderBackpropagationCurse of dimensionalityDegree matrix

Papers overview

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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
2009
2009
Rank transformation and manifold learning for multivariate mathematical morphology
The extension of lattice based operators to multivariate images is still a challenging theme in mathematical morphology. In this… 
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… 
2007
2007
A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding
Neighborhood Preserving Embedding (NPE) is an unsupervised manifold learning algorithm with subspace learning characteristic. 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… 
2006
2006
Incremental Construction of Neighborhood Graphs for Nonlinear Dimensionality Reduction
Most nonlinear data embedding methods use bottom-up approaches for capturing underlying structures of data distributed as points… 
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… 
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