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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AutoencoderBackpropagationCurse of dimensionalityDegree matrix
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Papers overview

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Highly Cited
2014
Highly Cited
2014
Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction
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
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… (More)
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Highly Cited
2006
Highly Cited
2006
Incremental nonlinear dimensionality reduction by manifold learning
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
Supervised nonlinear dimensionality reduction for visualization and classification
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
Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization
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
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… (More)
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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 data… (More)
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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… (More)
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Highly Cited
2002
Highly Cited
2002
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… (More)
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Highly Cited
2002
Highly Cited
2002
Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment
Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization… (More)
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