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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… Expand
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Related topics

Related topics

42 relations
AutoencoderBackpropagationCurse of dimensionalityDegree matrix
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2020
Review
2020
A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains
High-speed trains have become one of the most important and advanced branches of intelligent transportation, of which the… Expand
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Review
2018
Review
2018
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
Abstract Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input… Expand
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Highly Cited
2010
Highly Cited
2010
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, although the existing methods have… Expand
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Highly Cited
2007
Highly Cited
2007
Nonlinear Dimensionality Reduction
Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional… Expand
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Highly Cited
2005
Highly Cited
2005
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized data… Expand
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Highly Cited
2004
Highly Cited
2004
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting… Expand
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Highly Cited
2004
Highly Cited
2004
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta… Expand
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Highly Cited
2003
Highly Cited
2003
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex… Expand
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Highly Cited
2003
Highly Cited
2003
Nonlinear Dimension Reduction via Local Tangent Space Alignment
In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized… Expand
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Highly Cited
2002
Highly Cited
2002
Global Versus Local Methods in Nonlinear Dimensionality Reduction
Recently proposed algorithms for nonlinear dimensionality reduction fall broadly into two categories which have different… Expand
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