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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Gorban, Alexander N. (Επιμελητής έκδοσης), Kégl, Balázs (Επιμελητής έκδοσης), Wunsch, Donald C. (Επιμελητής έκδοσης), Zinovyev, Andrei Y. (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
Σειρά:Lecture Notes in Computational Science and Enginee, 58
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.