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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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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2020
Review
2020
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
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
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
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
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
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
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
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
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
Recently proposed algorithms for nonlinear dimensionality reduction fall broadly into two categories which have different… Expand
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