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RSSPrincipal Manifolds for Data Visualization and Dimension Reduction | ||||||||||||||||||
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| Table of contents 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 DRM-restrictions Printing: not available Clipboard copying: not available | ||||||||||||||||||
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