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.
Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition
Subtitle of host publicationJoint IAPR International Workshop, S+SSPR 2016, proceedings
EditorsA. Robles-Kelly, Marco Loog, B. Biggio, F. Escolano, R. Wilson
Place of PublicationCham
PublisherSpringer
Pages84-94
Number of pages11
ISBN (Electronic)978-3-319-49055-7
ISBN (Print)978-3-319-49054-0
DOIs
Publication statusPublished - 2016
EventSSPR Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) - Mérida, Mexico
Duration: 29 Nov 20162 Dec 2016

Publication series

NameLecture Notes in Computer Science
Volume10029
ISSN (Print)0302-9743

Workshop

WorkshopSSPR Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)
CountryMexico
CityMérida
Period29/11/162/12/16

ID: 11606989