The Similarity Between Dissimilarities

Conference paper (2016)

Authors

D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS
V.V. Cheplygina Pattern Recognition and Bioinformatics - EEMCS , Erasmus MC
R.P.W. Duin Pattern Recognition and Bioinformatics - EEMCS
Jan van de Poll Transparency Lab
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

When characterizing teams of people, molecules, or general graphs, it is difficult to encode all information using a single feature vector only. For these objects dissimilarity matrices that do capture the interaction or similarity between the sub-elements (people, atoms, nodes), can be used. This paper compares several representations of dissimilarity matrices, that encode the cluster characteristics, latent dimensionality, or outliers of these matrices. It appears that both the simple eigenvalue spectrum, or histogram of distances are already quite effective, and are able to reach high classification performances in multiple instance learning (MIL) problems. Finally, an analysis on teams of people is given, illustrating the potential use of dissimilarity matrix characterization for business consultancy.