This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples.
The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings.
The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.
John A. Lee is a Postdoctoral Researcher of the Belgian National Fund for Scientific Research (FNRS). He is (co-)author of more than 30 publications in the field ofmachine learning and dimensionality reduction. Michel Verleysen is Professor at the Université catholique de Louvain (Louvain-la-Neuve, Belgium), and Honorary Research Director of the Belgian National Fund for Scientific Research (FNRS). He is the chairman of the annual European Symposium on Artificial Neural Networks, co-editor of the Neural Processing Letters journal (Springer), and (co-)author of more than 200 scientific publications in the field of machine learning.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.