Saarland University, Machine Learning Group, Fak. MI - Mathematik und Informatik, Campus E1 1, 66123 Saarbr├╝cken, Germany     

Machine Learning Group
Department of Mathematics and Computer Science - Saarland University


a Matlab GUI to explore similarity graphs

by Matthias Hein and Ulrike von Luxburg


DemoSimilarityGraphs: Given a data set and a similarity function, there are many different ways how a similarity graph can be constructed: epsilon-neighborhood graphs, k-nearest neighbor graphs in different flavors, completely connected graphs, weighted or unweighted graphs, and many more . Additionally, most of those graphs come with a parameter which has to be chosen. The purpose of this demo is to show how different neighborhood graphs can behave on the same data set (see below for more details).


DemoSimilarityGraphs has been used for teaching purposes at the Machine Learning Summer School 2007, at the Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany. The tutorial introduces the different neighborhood graphs and demonstrates properties and some surprising effects using the tool.

Download the tutorial on similarity graphs   

SCREENSHOT OF DemoSimilarityGraphs

PANELS IN DemoSimilarityGraphs