Principal Manifolds for Data Visualization and Dimension Reduction

In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SO...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Gorban, Alexander N. (Editor ), Kégl, Balázs (Editor ), Wunsch, Donald C. (Editor ), Zinovyev, Andrei (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Lecture Notes in Computational Science and Engineering, 58
Temas:
Control engineering.
Social sciences.
Humanities.
Mathematics-Data processing.
Mathematical physics.
Computational intelligence.
Statistics .
Control and Systems Theory.
Humanities and Social Sciences.
Computational Science and Engineering.
Mathematical Methods in Physics.
Computational Intelligence.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Developments and Applications of Nonlinear Principal Component Analysis - a Review
  • Nonlinear Principal Component Analysis: Neural Network Models and Applications
  • Learning Nonlinear Principal Manifolds by Self-Organising Maps
  • Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization
  • Topology-Preserving Mappings for Data Visualisation
  • The Iterative Extraction Approach to Clustering
  • Representing Complex Data Using Localized Principal Components with Application to Astronomical Data
  • Auto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection Pursuit
  • Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes
  • Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms
  • On Bounds for Diffusion, Discrepancy and Fill Distance Metrics
  • Geometric Optimization Methods for the Analysis of Gene Expression Data
  • Dimensionality Reduction and Microarray Data
  • PCA and K-Means Decipher Genome.

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