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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin :
Springer,
2007.
|
| Colección: | Lecture notes in computational science and engineering ;
58. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Principal manifolds for data visualization and dimension reduction / |c Alexander N. Gorban [and others], editors. |
| 260 | |a Berlin : |b Springer, |c 2007. | ||
| 300 | |a 1 online resource (xxiii, 334 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Lecture notes in computational science and engineering ; |v 58 | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Front Matter; 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. | |
| 520 | |a 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 (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology pre. | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Principal components analysis. | |
| 650 | 0 | |a Statistics |x Graphic methods. | |
| 650 | 0 | |a Dimension reduction (Statistics) | |
| 650 | 6 | |a Analyse en composantes principales. | |
| 650 | 6 | |a Statistique |x Méthodes graphiques. | |
| 650 | 6 | |a Réduction de dimension (Statistique) | |
| 650 | 7 | |a MATHEMATICS |x Graphic Methods. |2 bisacsh | |
| 650 | 0 | 7 | |a Principal components analysis. |2 cct |
| 650 | 0 | 7 | |a Statistics |x Graphic methods. |2 cct |
| 650 | 0 | 7 | |a Mathematical Methods in Physics. |2 cct |
| 650 | 0 | 7 | |a Numerical and Computational Methods in Engineering. |2 cct |
| 650 | 7 | |a Estadística |2 embne | |
| 650 | 0 | 7 | |a Análisis en componentes principales |2 embucm |
| 650 | 7 | |a Dimension reduction (Statistics) |2 fast | |
| 650 | 7 | |a Principal components analysis |2 fast | |
| 650 | 7 | |a Statistics |x Graphic methods |2 fast | |
| 700 | 1 | |a Gorbanʹ, A. N. |q (Aleksandr Nikolaevich) |1 https://id.oclc.org/worldcat/entity/E39PBJyMmDt83XXFjyVTPtpXVC | |
| 758 | |i has work: |a Principal manifolds for data visualization and dimension reduction (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGDCTHwYckxpC44yGbJxDq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | |t Principal manifolds for data visualization and dimension reduction. |w (OCoLC)166372536 | |
| 830 | 0 | |a Lecture notes in computational science and engineering ; |v 58. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=337417 |z Texto completo |
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