Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction
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Published By Springer Berlin Heidelberg

9783540737490, 9783540737506

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Representing Complex Data Using Localized Principal Components with Application to Astronomical Data

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_7 ◽

2008 ◽

pp. 178-201 ◽

Cited By ~ 5

Author(s):

Jochen Einbeck ◽

Ludger Evers ◽

Coryn Bailer-Jones

Keyword(s):

Principal Components ◽

Complex Data ◽

Astronomical Data

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PCA and K-Means Decipher Genome

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_14 ◽

2008 ◽

pp. 309-323 ◽

Cited By ~ 1

Author(s):

Alexander N. Gorban ◽

Andrei Y. Zinovyev

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Learning Nonlinear Principal Manifolds by Self-Organising Maps

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_3 ◽

2008 ◽

pp. 68-95 ◽

Cited By ~ 6

Author(s):

Hujun Yin

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Developments and Applications of Nonlinear Principal Component Analysis – a Review

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_1 ◽

2008 ◽

pp. 1-43 ◽

Cited By ~ 19

Author(s):

Uwe Kruger ◽

Junping Zhang ◽

Lei Xie

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Nonlinear Principal Component Analysis

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Geometric Optimization Methods for the Analysis of Gene Expression Data

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_12 ◽

2008 ◽

pp. 271-292 ◽

Cited By ~ 1

Author(s):

Michel Journée ◽

Andrew E. Teschendorff ◽

Pierre-Antoine Absil ◽

Simon Tavaré ◽

Rodolphe Sepulchre

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Optimization Methods ◽

Geometric Optimization ◽

Expression Data

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Auto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection Pursuit

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_8 ◽

2008 ◽

pp. 202-218 ◽

Cited By ~ 1

Author(s):

Stéphane Girard ◽

Serge Iovleff

Keyword(s):

Principal Component Analysis ◽

Projection Pursuit ◽

Principal Component ◽

Component Analysis ◽

Nonlinear Principal Component Analysis ◽

Associative Models

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On Bounds for Diffusion, Discrepancy and Fill Distance Metrics

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_11 ◽

2008 ◽

pp. 261-270

Author(s):

Steven B. Damelin

Keyword(s):

Distance Metrics

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The Iterative Extraction Approach to Clustering

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_6 ◽

2008 ◽

pp. 151-177 ◽

Cited By ~ 1

Author(s):

Boris Mirkin

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Topology-Preserving Mappings for Data Visualisation

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_5 ◽

2008 ◽

pp. 131-150 ◽

Cited By ~ 2

Author(s):

Marian Pena ◽

Wesam Barbakh ◽

Colin Fyfe

Keyword(s):

Data Visualisation

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Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction ◽

10.1007/978-3-540-73750-6_10 ◽

2008 ◽

pp. 238-260 ◽

Cited By ~ 17

Author(s):

Boaz Nadler ◽

Stephane Lafon ◽

Ronald Coifman ◽

Ioannis G. Kevrekidis

Keyword(s):

Clustering Algorithms ◽

Probabilistic Interpretation ◽

Diffusion Maps ◽

Spectral Embedding

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Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension ReductionLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Representing Complex Data Using Localized Principal Components with Application to Astronomical Data

PCA and K-Means Decipher Genome

Learning Nonlinear Principal Manifolds by Self-Organising Maps

Developments and Applications of Nonlinear Principal Component Analysis – a Review

Geometric Optimization Methods for the Analysis of Gene Expression Data

Auto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection Pursuit

On Bounds for Diffusion, Discrepancy and Fill Distance Metrics

The Iterative Extraction Approach to Clustering

Topology-Preserving Mappings for Data Visualisation

Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms

Lecture Notes in Computational Science and Enginee - Principal Manifolds for Data Visualization and Dimension Reduction
Latest Publications