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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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Papers overview

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Highly Cited
2009
Highly Cited
2009
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use… 
2009
2009
The extension of lattice based operators to multivariate images is still a challenging theme in mathematical morphology. In this… 
2007
2007
Abstract : Geometric harmonics provides a framework for taking data in high-dimensional measurement spaces and embedding them in… 
2007
2007
Neighborhood Preserving Embedding (NPE) is an unsupervised manifold learning algorithm with subspace learning characteristic. In… 
Highly Cited
2006
Highly Cited
2006
Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D… 
2006
2006
Most nonlinear data embedding methods use bottom-up approaches for capturing underlying structures of data distributed as points… 
Highly Cited
2002
Highly Cited
2002
An algorithm for manifold learning is presented. Given only samples of a finite-dimensional differentiable manifold and no a…