DocumentCode
2172673
Title
Classifier-based affinities for clustering sets of vectors
Author
García-García, Darío ; Santos-Rodríguez, Raúl ; Parrado-Hernández, Emilio
Author_Institution
Ind. Syst. Unit, TECNALIA Res. & Innovation, Donostia - San Sebastián, Spain
fYear
2012
fDate
23-26 Sept. 2012
Firstpage
1
Lastpage
6
Abstract
We focus on the task of clustering sets of vectors. This can be seen as a special case of sequence clustering when the dynamics are not taken into account. We propose to use the error probability of binary classifiers to obtain a measure of the affinity between two sets so that a standard similarity-based clustering algorithm can be applied.
Keywords
error statistics; pattern classification; pattern clustering; vectors; binary classifiers; classifier-based affinities; error probability; similarity-based clustering algorithm; vector clustering sets; Hidden Markov models; Kernel; Measurement uncertainty; Probabilistic logic; Probability distribution; Standards; Vectors; Sequence clustering; sets of vectors; speaker clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on
Conference_Location
Santander
ISSN
1551-2541
Print_ISBN
978-1-4673-1024-6
Electronic_ISBN
1551-2541
Type
conf
DOI
10.1109/MLSP.2012.6349760
Filename
6349760
Link To Document