TY - GEN
T1 - A batch rival penalized em algorithm for gaussian mixture clustering with automatic model selection
AU - Zhang, Dan
AU - CHEUNG, Yiu Ming
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Cheung [2] has recently proposed a general learning framework, namely Maximum Weighted Likelihood (MWL), in which an adaptive Rival Penalized EM (RPEM) algorithm has been successfully developed for density mixture clustering with automatic model selection. Nevertheless, its convergence speed relies on the value of learning rate. In general, selection of an appropriate learning rate is a nontrivial task. To circumvent such a selection, this paper further studies the MWL learning framework, and develops a batch RPEM algorithm accordingly provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm, this batch RPEM need not assign the learning rate analogous to the EM, but still preserve the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM. The experiments show the efficacy of the proposed algorithm.
AB - Cheung [2] has recently proposed a general learning framework, namely Maximum Weighted Likelihood (MWL), in which an adaptive Rival Penalized EM (RPEM) algorithm has been successfully developed for density mixture clustering with automatic model selection. Nevertheless, its convergence speed relies on the value of learning rate. In general, selection of an appropriate learning rate is a nontrivial task. To circumvent such a selection, this paper further studies the MWL learning framework, and develops a batch RPEM algorithm accordingly provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm, this batch RPEM need not assign the learning rate analogous to the EM, but still preserve the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM. The experiments show the efficacy of the proposed algorithm.
KW - Learning rate
KW - Maximum weighted likelihood
KW - Rival penalized expectation-maximization algorithm
UR - http://www.scopus.com/inward/record.url?scp=37249044259&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-72458-2_31
DO - 10.1007/978-3-540-72458-2_31
M3 - Conference proceeding
AN - SCOPUS:37249044259
SN - 9783540724575
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 252
EP - 259
BT - Rough Sets and Knowledge Technology - Second International Conference, RSKT 2007, Proceedings
PB - Springer Verlag
T2 - 2nd International Conference on Rough Sets and Knowledge Technology, RSKT 2007
Y2 - 14 May 2007 through 16 May 2007
ER -