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...
Συγγραφή απο Οργανισμό/Αρχή: | |
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Άλλοι συγγραφείς: | , , , |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2008.
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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.