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Principal Manifolds for Data Visualization and Dimension Reduction
Lecture Notes in Computational Science and Engineering 58
ISBN/EAN: 9783540737490
Umbreit-Nr.: 352293
Sprache:
Englisch
Umfang: xxiv, 340 S., 68 s/w Illustr., 14 farbige Illustr.
Format in cm:
Einband:
kartoniertes Buch
Erschienen am 01.10.2007
Auflage: 1/2007
- Zusatztext
- InhaltsangabeDevelopments 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.
- Kurztext
- New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described Presentation of algorithms is supplemented by case studies