Donald C. Wunsch

Alexander N. Gorban

Andrei Zinovyev

Balázs Kégl

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.

Principal Manifolds for Data Visualization and Dimension Reduction

Iliya V. Karlin

By bringing together various ideas and methods for extracting the slow manifolds, the authors show that it is possible to establish a more macroscopic description in nonequilibrium systems. The book treats slowness as stability. A unifying geometrical viewpoint of the thermodynamics of slow and fast motion enables the development of reduction techniques, both analytical and numerical. Examples considered in the book range from the Boltzmann kinetic equation and hydrodynamics to the Fokker-Planck equations of polymer dynamics and models of chemical kinetics describing oxidation reactions. Special chapters are devoted to model reduction in classical statistical dynamics, natural selection, and exact solutions for slow hydrodynamic manifolds. The book will be a major reference source for both theoretical and applied model reduction. Intended primarily as a postgraduate-level text in nonequilibrium kinetics and model reduction, it will also be valuable to PhD students and researchers in applied mathematics, physics and various fields of engineering.

Invariant Manifolds for Physical and Chemical Kinetics

Jeremy Levesley

Elaine Fehrman

Vincent Egan

Evgeny M. Mirkes

Awaz K. Muhammad

This book discusses the psychological traits associated with drug consumption through the statistical analysis of a new database with information on 1885 respondents and use of 18 drugs. After reviewing published works on the psychological profiles of drug users and describing the data mining and machine learning methods used, it demonstrates that the personality traits (five factor model, impulsivity, and sensation seeking) together with simple demographic data make it possible to predict the risk of consumption of individual drugs with a sensitivity and specificity above 70% for most drugs. It also analyzes the correlations of use of different substances and describes the groups of drugs with correlated use, identifying significant differences in personality profiles for users of different drugs. The book is intended for advanced undergraduates and first-year PhD students, as well as researchers and practitioners. Although no previous knowledge of machine learning, advanced data mining concepts or modern psychology of personality is assumed, familiarity with basic statistics and some experience in the use of probabilities would be helpful. For a more detailed introduction to statistical methods, the book provides recommendations for undergraduate textbooks.

A Story Told by Data

Personality Traits and Drug Consumption

Andrea Cangiani

Ruslan L Davidchack

Emmanuil Georgoulis

Michael V. Tretyakov

The European Conferences on Numerical Mathematics and Advanced Applications (ENUMATH) are a series of conferences held every two years to provide a forum for discussion of new trends in numerical mathematics and challenging scientific and industrial applications at the highest level of international expertise. ENUMATH 2011 was hosted by the University of Leicester (UK) from the 5th to 9th September 2011. This proceedings volume contains more than 90 papers by speakers of the conference and gives an overview of recent developments in scientific computing, numerical analysis, and practical use of modern numerical techniques and algorithms in various applications. New results on finite element methods, multiscale methods, numerical linear algebra, and finite difference schemes are presented. A range of applications include computational problems from fluid dynamics, materials, image processing, and molecular dynamics.​

Proceedings of ENUMATH 2011, the 9th European Conference on Numerical Mathematics and Advanced Applications, Leicester, September 2011

Numerical Mathematics and Advanced Applications 2011

Alexander N. Gorban

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