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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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AutoencoderBackpropagationCurse of dimensionalityDegree matrix

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2013
2013
CERTH @ MediaEval 2013 Social Event Detection Task
This paper describes the participation of CERTH in the “Social Event Detection Task @ MediaEval 2011”, which aims at discovering… 
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
A new nonlinear dimensionality reduction method with application to hyperspectral image analysis
In this paper, we propose a new nonlinear dimensionality reduction method by combining Locally Linear Embedding (LLE) with… 
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
Anomaly Detection in Transportation Corridors using Manifold Embedding
The formation of secure transportation corridors, where cargoes and shipments from points of entry can be dispatched safely to… 
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
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… 
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