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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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