TY - GEN
T1 - An Enhanced em algorithm using maximum entropy distribution as initial condition
AU - Xuan, Guorong
AU - Shi, Yun Q.
AU - Chai, Peiqi
AU - Sutthiwan, Patchara
PY - 2012
Y1 - 2012
N2 - The conventional EM algorithms may suffer from the following two problems. First, it may converge to a local maximum. Second, the algorithm may suffer from singularity. A novel Enhanced EM algorithm (EEM) using a realization of maximum-entropy uniform distribution as initial condition is proposed. A global optimal solution can be obtained. In addition, a positive perturbation scheme is adopted to avoid singularity. Experimental results have demonstrated that the EEM is simple and effective compared with some prior arts.
AB - The conventional EM algorithms may suffer from the following two problems. First, it may converge to a local maximum. Second, the algorithm may suffer from singularity. A novel Enhanced EM algorithm (EEM) using a realization of maximum-entropy uniform distribution as initial condition is proposed. A global optimal solution can be obtained. In addition, a positive perturbation scheme is adopted to avoid singularity. Experimental results have demonstrated that the EEM is simple and effective compared with some prior arts.
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M3 - Conference contribution
AN - SCOPUS:84874564786
SN - 9784990644109
T3 - Proceedings - International Conference on Pattern Recognition
SP - 849
EP - 852
BT - ICPR 2012 - 21st International Conference on Pattern Recognition
T2 - 21st International Conference on Pattern Recognition, ICPR 2012
Y2 - 11 November 2012 through 15 November 2012
ER -