TY - GEN
T1 - Discriminative Exemplar clustering
AU - Yang, Yingzhen
AU - Liang, Feng
AU - Huang, Thomas S.
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - Exemplar-based clustering methods partition the data space and identify the representative, or the exemplar, of each cluster. With the number of clusters adaptively determined, exemplar-based clustering methods are appealing since they avoid or alleviate the difficult task of estimating the latent parameters in case of complex models and high dimensionality of the data. Most exemplar-based clustering methods are based on generative models, where the exemplars serve as the parameters of the generative models. However, generative models do not consider the discriminative capability of the cluster boundaries explicitly described in discriminative models. In this paper, we present Discriminative Exemplar Clustering (DEC), that improves the discriminative power of exemplar-based clustering method by minimizing the misclassification error of the nonparametric unsupervised plug-in classifier while maintaining the appealing property of exemplar-based clustering. The optimization of DEC is performed in a pairwise Markov Random Field. Experimental results on synthetic and real data demonstrate the effectiveness of our method compared to other exemplar-based clustering methods.
AB - Exemplar-based clustering methods partition the data space and identify the representative, or the exemplar, of each cluster. With the number of clusters adaptively determined, exemplar-based clustering methods are appealing since they avoid or alleviate the difficult task of estimating the latent parameters in case of complex models and high dimensionality of the data. Most exemplar-based clustering methods are based on generative models, where the exemplars serve as the parameters of the generative models. However, generative models do not consider the discriminative capability of the cluster boundaries explicitly described in discriminative models. In this paper, we present Discriminative Exemplar Clustering (DEC), that improves the discriminative power of exemplar-based clustering method by minimizing the misclassification error of the nonparametric unsupervised plug-in classifier while maintaining the appealing property of exemplar-based clustering. The optimization of DEC is performed in a pairwise Markov Random Field. Experimental results on synthetic and real data demonstrate the effectiveness of our method compared to other exemplar-based clustering methods.
KW - Exemplar-based Clustering
KW - Pairwise Markov Random Fields
UR - http://www.scopus.com/inward/record.url?scp=84905233545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905233545&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854911
DO - 10.1109/ICASSP.2014.6854911
M3 - Conference contribution
AN - SCOPUS:84905233545
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6771
EP - 6775
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -