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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| Autres auteurs: | , , |
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Collection: | Lecture Notes in Computational Science and Enginee
58 Lecture Notes in Computational Science and Engineering 58 |
| Sujets: | |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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L'impression du document génère 360 p. Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Principal manifolds for data visualization and dimension reduction, Alexander N. Gorban, ... [et al.], editors, 2008, Berlin, Springer, 1 vol. (XXIII-334 p.), Lecture notes in computational science and engineering, 978-3-540-73749-0 |
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| 084 | |a 2H25. 2010 | ||
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| 111 | 2 | |a Principal manifolds for data cartography and dimension reduction |d (2006 |c :Leicester, Royaume-Unis). | |
| 245 | 1 | 0 | |a Principal Manifolds for Data Visualization and Dimension Reduction |c [workshop on Principal manifolds for data cartography and dimension reduction] ; Alexander N. Gorban, Balázs Kégl, Donald C. Wunsch,... [et al.], (Eds.). |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Lecture Notes in Computational Science and Enginee |v 58 |x 1439-7358 | |
| 490 | 1 | |a Lecture Notes in Computational Science and Engineering |v 58 | |
| 500 | |a L'impression du document génère 360 p. | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 504 | |a Notes bibliogr. Index | ||
| 505 | 1 | |a 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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 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 preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial "PCA and K-means decipher genome". The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics | ||
| 650 | |a Statistique |x Méthodes graphiques | ||
| 650 | |a Géométrie |x Informatique | ||
| 650 | |a Analyse en composantes principales | ||
| 700 | 1 | |a Gorban, Alexander Nikolaevich, |d 1952-2025. |4 edt | |
| 700 | 1 | |a Kégl, Balázs. |4 edt | |
| 700 | 1 | |a Wunsch, Donald C. |4 edt | |
| 776 | 0 | |0 139596682 |t Principal manifolds for data visualization and dimension reduction |f Alexander N. Gorban, ... [et al.], editors |d 2008 |c Berlin |n Springer |p 1 vol. (XXIII-334 p.) |s Lecture notes in computational science and engineering |z 978-3-540-73749-0 | |
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