TY - GEN
T1 - The Similarity Between Dissimilarities
AU - Tax, David
AU - Cheplygina, Veronika
AU - Duin, Bob
AU - van de Poll, Jan
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-49055-7_8
DO - 10.1007/978-3-319-49055-7_8
M3 - Conference contribution
SN - 978-3-319-49054-0
T3 - Lecture Notes in Computer Science
SP - 84
EP - 94
BT - Structural, Syntactic, and Statistical Pattern Recognition
A2 - Robles-Kelly, A.
A2 - Loog, Marco
A2 - Biggio, B.
A2 - Escolano, F.
A2 - Wilson, R.
PB - Springer
CY - Cham
T2 - SSPR Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)
Y2 - 29 November 2016 through 2 December 2016
ER -