Principal Manifolds for Data Visualization and Dimension Reduction

ISBN: 978-3-540-73749-0 (Print) 978-3-540-73750-6 (Online)

Table of contents (14 chapters)

  1. Front Matter

    Pages I-XXIII

  2. No Access

    Book Chapter

    Pages 1-43

    Developments and Applications of Nonlinear Principal Component Analysis – a Review

  3. No Access

    Book Chapter

    Pages 44-67

    Nonlinear Principal Component Analysis: Neural Network Models and Applications

  4. No Access

    Book Chapter

    Pages 68-95

    Learning Nonlinear Principal Manifolds by Self-Organising Maps

  5. No Access

    Book Chapter

    Pages 96-130

    Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization

  6. No Access

    Book Chapter

    Pages 131-150

    Topology-Preserving Mappings for Data Visualisation

  7. No Access

    Book Chapter

    Pages 151-177

    The Iterative Extraction Approach to Clustering

  8. No Access

    Book Chapter

    Pages 178-201

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

  9. No Access

    Book Chapter

    Pages 202-218

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

  10. No Access

    Book Chapter

    Pages 219-237

    Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes

  11. No Access

    Book Chapter

    Pages 238-260

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

  12. No Access

    Book Chapter

    Pages 261-270

    On Bounds for Diffusion, Discrepancy and Fill Distance Metrics

  13. No Access

    Book Chapter

    Pages 271-292

    Geometric Optimization Methods for the Analysis of Gene Expression Data

  14. No Access

    Book Chapter

    Pages 293-308

    Dimensionality Reduction and Microarray Data

  15. No Access

    Book Chapter

    Pages 309-323

    PCA and K-Means Decipher Genome

  16. Back Matter

    Pages 325-334