Публикация:Gorban (2008), Principal Manifolds
Материал из MachineLearning.
Gorban, A.N., Kegl, B., Wunsch, D., Zinovyev, A.Y. 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. and Kegl, B. and Wunsch, D. and Zinovyev, A.Y.",
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
