New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.

... . Griebel D. E. Keyes Alexander N. Gorban Balázs Kégl Donald C. Wunsch Andrei Zinovyev Editors R. M. Nieminen D. Roose T. Schlick Principal Manifolds for Data Visualization and Dimension Reduction Springer 2.4 Circular PCA . 51. Front ...

"This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.

Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings.

This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns.

Some chapters also contain exercises. Figures from the text and scripts/data for reproducing a breast cancer data analysis are available at www.cancer-systems-biology.net.

This book describes a cluster analytic methodology for generating linguistic hypotheses on the basis of data abstracted from language corpora.

... Principal Manifolds for Data Visualization and Dimension Reduction ( Lecture Notes in Computational Science and Engineering ) , 58 : 238-260 , 2007 . [ 128 ] B. Nadler , S. Lafon , R. R. Coifman , and I. G. Kevrekidis . Diffusion maps ...

... Principal manifolds for data visualization and dimension reduction . Berlin ; New York : Springer . xxiii , 334 pp . Grace JA , Amin N , Singh NC , Theunissen FE . 2003. Selectivity for conspecific song in the zebra finch auditory ...

The book contributions are organized under four major themes: Applications of Uncertainty in Aerospace & Engineering Imprecise Probability, Theory and Applications Robust and Reliability-Based Design Optimisation in Aerospace Engineering ...