Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixture density estimation with group membership functions
Pattern Recognition Letters
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates
Journal of VLSI Signal Processing Systems
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted tests of homogeneity for testing the number of components in a mixture
Computational Statistics & Data Analysis
Fitting of mixtures with unspecified number of components using cross validation distance estimate
Computational Statistics & Data Analysis
Estimating the number of components in a finite mixture model: the special case of homogeneity
Computational Statistics & Data Analysis
SMEM Algorithm for Mixture Models
Neural Computation
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Computer Vision and Image Understanding
Identification of nuclear magnetic resonance signals via gaussian mixture decomposition
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Random swap EM algorithm for Gaussian mixture models
Pattern Recognition Letters
| Hi-index | 0.10 |
The main difficulty with EM algorithm for mixture model concerns the number of components, say g. This is the question of model selection, and the EM algorithm itself could not estimate g. On the contrary, the algorithm requires g to be specified before the remaining parameters can be estimated. To solve this problem, a new algorithm, which is called stepwise split-and-merge EM (SSMEM) algorithm, is proposed. The SSMEM algorithm alternately splits and merges components, estimating g and other parameters of components simultaneously. Also, two novel criteria are introduced to efficiently select the components for split or merge. Experimental results on simulated and real data demonstrate the effectivity of the proposed algorithm.