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… (More)
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Papers overview

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Highly Cited
2014
Highly Cited
2014
This paper proposes to use autoencoders with nonlinear dimensionality reduction in the anomaly detection task. The authors apply… (More)
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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… (More)
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Highly Cited
2006
Highly Cited
2006
Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data… (More)
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Highly Cited
2005
Highly Cited
2005
When performing visualization and classification, people often confront the problem of dimensionality reduction. Isomap is one of… (More)
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Highly Cited
2005
Highly Cited
2005
We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming and kernel matrix factorization… (More)
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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… (More)
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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 data… (More)
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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… (More)
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Highly Cited
2002
Highly Cited
2002
One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex… (More)
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Highly Cited
2002
Highly Cited
2002
Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization… (More)
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