Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs...

The 8th International Congress of Ecology was held in Seoul, South Korea in August 2002, and was hosted by the Ecological Society of Korea. The Congress theme was 'Ecological Issues in a Changing World', and this volume includes selected contributions to illustrate some of the important topics which were discussed during the Congress.

Problems of scale have exercised the minds of ecologists for many years, and will continue to do so into the future. This volume deals with this subject and with mathematical approaches to improve our understanding of complex ecological systems. The...

Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs...