Principal Manifolds for Data Visualization and Dimension Reduction

1. Developments and Applications of Nonlinear Principal Component Analysis – a Review
Uwe Kruger, Junping Zhang, Lei Xie

2. Nonlinear Principal Component Analysis: Neural Network Models and Applications
Matthias Scholz, Martin Fraunholz, Joachim Selbig

3. Learning Nonlinear Principal Manifolds by Self-Organising Maps
Hujun Yin

4. Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization
Alexander N. Gorban, Andrei Y. Zinovyev

5. Topology-Preserving Mappings for Data Visualisation
Marian Pena, Wesam Barbakh, Colin Fyfe

6. The Iterative Extraction Approach to Clustering
Boris Mirkin

7. Representing Complex Data Using Localized Principal Components with Application to Astronomical Data
Jochen Einbeck, Ludger Evers, Coryn Bailer-Jones

8. Auto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection Pursuit
Stéphane Girard, Serge Iovleff

9. Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes
Alexander N. Gorban, Neil R. Sumner, Andrei Y. Zinovyev

10. Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms
Boaz Nadler, Stephane Lafon, Ronald Coifman, Ioannis G. Kevrekidis

11. On Bounds for Diffusion, Discrepancy and Fill Distance Metrics
Steven B. Damelin

12. Geometric Optimization Methods for the Analysis of Gene Expression Data
Michel Journée, Andrew E. Teschendorff, Pierre-Antoine Absil, Simon Tavaré, Rodolphe Sepulchre

13. Dimensionality Reduction and Microarray Data
David A. Elizondo, Benjamin N. Passow, Ralph Birkenhead, Andreas Huemer

14. PCA and K-Means Decipher Genome
Alexander N. Gorban, Andrei Y. Zinovyev

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Keywords: MATHEMATICS / General MAT000000