Публикация:Gorban (2008), Principal Manifolds

Материал из MachineLearning.

(Различия между версиями)
Перейти к: навигация, поиск
м (→Аннотация: оформление)
м (оформление)
Строка 1: Строка 1:
<includeonly>{{Монография|PageName = П:A. Gorban, B. Kegl, D. Wunsch, A. Zinovyev (2008), Principal Manifolds for Data Visualisation and Dimension Reduction
<includeonly>{{Монография|PageName = П:A. Gorban, B. Kegl, D. Wunsch, A. Zinovyev (2008), Principal Manifolds for Data Visualisation and Dimension Reduction
-
|автор = Gorban, A.N. (Ed.)
+
|автор = Gorban, A.N.
-
|автор2 = Kegl, B (Ed.)
+
|автор2 = Kegl, B
-
|автор3 = Wunsch, D (Ed.)
+
|автор3 = Wunsch, D
-
|автор4 = Zinovyev, A.Y. (Ed.)
+
|автор4 = Zinovyev, A.Y.
|название = Principal Manifolds for Data Visualisation and Dimension Reduction
|название = Principal Manifolds for Data Visualisation and Dimension Reduction
|издатель = Springer, Berlin – Heidelberg – New York
|издатель = Springer, Berlin – Heidelberg – New York
Строка 29: Строка 29:
1 Developments and Applications of Nonlinear Principal Component Analysis – a Review
1 Developments and Applications of Nonlinear Principal Component Analysis – a Review
-
Uwe Kruger, Junping Zhang, Lei Xie
+
 +
Uwe Kruger, Junping Zhang, Lei Xie
 +
 
2 Nonlinear Principal Component Analysis: Neural Network Models and Applications
2 Nonlinear Principal Component Analysis: Neural Network Models and Applications
-
Matthias Scholz, Martin Fraunholz, Joachim Selbig
+
 +
Matthias Scholz, Martin Fraunholz, Joachim Selbig
3 Learning Nonlinear Principal Manifolds by Self-Organising Maps
3 Learning Nonlinear Principal Manifolds by Self-Organising Maps
-
Hujun Yin
+
 +
Hujun Yin
 +
 
4 Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data visualization
4 Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data visualization
-
Alexander N Gorban, Andrei Y Zinovyev
+
 +
Alexander N Gorban, Andrei Y Zinovyev
 +
 
5 Topology-Preserving Mappings for Data Visualisation
5 Topology-Preserving Mappings for Data Visualisation
-
Marian PeЇna, Wesam Barbakh, Colin Fyfe
+
 +
Marian PeЇna, Wesam Barbakh, Colin Fyfe
 +
 
6 The Iterative Extraction Approach to Clustering
6 The Iterative Extraction Approach to Clustering
-
Boris Mirkin
+
 +
Boris Mirkin
 +
 
7 Representing Complex Data Using Localized Principal Components with Application to Astronomical Data
7 Representing Complex Data Using Localized Principal Components with Application to Astronomical Data
-
Jochen Einbeck, Ludger Evers, Coryn Bailer-Jones
+
 +
Jochen Einbeck, Ludger Evers, Coryn Bailer-Jones
 +
 
8 Auto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection Pursuit
8 Auto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection Pursuit
-
Stґephane Girard, Serge Iovleff
+
 +
Stґephane Girard, Serge Iovleff
 +
 
9 Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes
9 Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes
-
Alexander N Gorban, Neil R Sumner, Andrei Y Zinovyev
+
 +
Alexander N Gorban, Neil R Sumner, Andrei Y Zinovyev
 +
 
10 Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms
10 Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms
-
Boaz Nadler, Stephane Lafon, Ronald Coifman, Ioannis G Kevrekidis
+
 +
Boaz Nadler, Stephane Lafon, Ronald Coifman, Ioannis G Kevrekidis
 +
 
11 On Bounds for Diffusion, Discrepancy and Fill Distance Metrics
11 On Bounds for Diffusion, Discrepancy and Fill Distance Metrics
-
Steven B Damelin
+
 +
Steven B Damelin
12 Geometric Optimization Methods for the Analysis of Gene Expression Data
12 Geometric Optimization Methods for the Analysis of Gene Expression Data
-
Michel Journґee, Andrew E Teschendorff, Pierre-Antoine Absil, Simon Tavarґe, Rodolphe Sepulchre
+
 +
Michel Journґee, Andrew E Teschendorff, Pierre-Antoine Absil, Simon Tavarґe, Rodolphe Sepulchre
 +
 
 +
 
13 Dimensionality Reduction and Microarray data
13 Dimensionality Reduction and Microarray data
-
David A Elizondo, Benjamin N Passow, Ralph Birkenhead, Andreas Huemer
+
 +
David A Elizondo, Benjamin N Passow, Ralph Birkenhead, Andreas Huemer
 +
 
14 PCA and K-Means Decipher Genome
14 PCA and K-Means Decipher Genome
-
Alexander N Gorban, Andrei Y Zinovyev
+
 +
Alexander N Gorban, Andrei Y Zinovyev
 +
 
== Ссылки ==
== Ссылки ==
*[http://pca.narod.ru/ Нелинейный метод главных компонент]
*[http://pca.narod.ru/ Нелинейный метод главных компонент]
*[http://www.springer.com/math/cse/book/978-3-540-73749-0 Principal Manifolds for Data Visualization and Dimension Reduction]
*[http://www.springer.com/math/cse/book/978-3-540-73749-0 Principal Manifolds for Data Visualization and Dimension Reduction]
-
[[Категория:Учебники]]
 
[[Категория:Машинное обучение (публикации)]]
[[Категория:Машинное обучение (публикации)]]
</noinclude>
</noinclude>
}}
}}

Версия 14:17, 26 сентября 2009

Gorban, A.N. (Ed.), Kegl, B (Ed.), Wunsch, D (Ed.), Zinovyev, A.Y. (Ed.) Principal Manifolds for Data Visualisation and Dimension Reduction. — Springer, Berlin – Heidelberg – New York, 2008. — ISBN 978-3-540-73749-0

BibTeX: 
 @book{NPCA2007,
   author = "Gorban, A.N. (Ed.) and Kegl, B (Ed.) and Wunsch, D (Ed.) and Zinovyev, A.Y. (Ed.)",
   title = "Principal Manifolds for Data Visualisation and Dimension Reduction",
   publisher = "Springer, Berlin – Heidelberg – New York",
   year = "2008",
   url = "http://pca.narod.ru/contentsgkwz.htm",
   isbn = "978-3-540-73749-0",
   language = english
 }

Аннотация

Первая в мировой научной литературе монография, посвященная методу главных многообразий (обобщения Кохоненовских SOM в том числе): Главные многообразия для визуализации и анализа данных, А. Горбань, Б. Кегль, Д. Вунш, А. Зиновьев (ред.), Шпрингер, 2007. Подготовлена международным коллективом авторов.


Contents

1 Developments and Applications of Nonlinear Principal Component Analysis – a Review

Uwe Kruger, Junping Zhang, Lei Xie


2 Nonlinear Principal Component Analysis: Neural Network Models and Applications

Matthias Scholz, Martin Fraunholz, Joachim Selbig


3 Learning Nonlinear Principal Manifolds by Self-Organising Maps

Hujun Yin


4 Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data visualization

Alexander N Gorban, Andrei Y Zinovyev


5 Topology-Preserving Mappings for Data Visualisation

Marian PeЇna, Wesam Barbakh, Colin Fyfe


6 The Iterative Extraction Approach to Clustering

Boris Mirkin


7 Representing Complex Data Using Localized Principal Components with Application to Astronomical Data

Jochen Einbeck, Ludger Evers, Coryn Bailer-Jones


8 Auto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection Pursuit

Stґephane Girard, Serge Iovleff


9 Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes

Alexander N Gorban, Neil R Sumner, Andrei Y Zinovyev


10 Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms

Boaz Nadler, Stephane Lafon, Ronald Coifman, Ioannis G Kevrekidis


11 On Bounds for Diffusion, Discrepancy and Fill Distance Metrics

Steven B Damelin

12 Geometric Optimization Methods for the Analysis of Gene Expression Data

Michel Journґee, Andrew E Teschendorff, Pierre-Antoine Absil, Simon Tavarґe, Rodolphe Sepulchre


13 Dimensionality Reduction and Microarray data

David A Elizondo, Benjamin N Passow, Ralph Birkenhead, Andreas Huemer


14 PCA and K-Means Decipher Genome

Alexander N Gorban, Andrei Y Zinovyev


Ссылки

}}

Источник — «http://machinelearning.ru/wiki/index.php?title=%D0%9F%D1%83%D0%B1%D0%BB%D0%B8%D0%BA%D0%B0%D1%86%D0%B8%D1%8F:Gorban_%282008%29%2C_Principal_Manifolds»
Личные инструменты