EM algorithms for Gaussian mixtures with split-and-merge operation
نویسندگان
چکیده
In order to alleviate the problem of local convergence of the usual EM algorithm, a split-and-merge operation is introduced into the EM algorithm for Gaussian mixtures. The split-and-merge equations are 7rst presented theoretically. These equations show that the merge operation is a well-posed problem, whereas the split operation is an ill-posed problem because it is the inverse procedure of the merge. Two methods for solving this ill-posed problem are developed through the singular value decomposition and the Cholesky decomposition. Accordingly, a new modi7ed EM algorithm is constructed. Our experiments demonstrate that this algorithm is e:cient for unsupervised color image segmentation. ? 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
منابع مشابه
A Dynamic Merge-or-Split Learning Algorithm on Gaussian Mixture for Automated Model Selection
Gaussian mixture modelling is a powerful tool for data analysis. However, the selection of number of Gaussians in the mixture, i.e., the mixture model or scale selection, remains a difficult problem. In this paper, we propose a new kind of dynamic merge-or-split learning (DMOSL) algorithm on Gaussian mixture such that the number of Gaussians can be determined automatically with a dynamic merge-...
متن کاملA Practical Method for Self-adapting Gaussian Expectation Maximization
Split-and-merge techniques have been demonstrated to be effective in overtaking the convergence problems in classical EM. In this paper we follow a split-and-merge approach and we propose a new EM algorithm that makes use of a on-line variable number of mixture Gaussians components. We introduce a measure of the similarities to decide when to merge components. A set of adaptive thresholds keeps...
متن کاملSplit and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates
The EM algorithm for Gaussian mixture models often gets caught in local maxima of the likelihood which involve having too many Gaussians in one part of the space and too few in another, widely separated part of the space. We present a new EM algorithm which performs split and merge operations on the Gaussians to escape from these configurations. This algorithm uses two novel criteria for effici...
متن کاملSpoken Language Identification for Indian Languages Using Split and Merge EM Algorithm
Performance of Language Identification (LID) System using Gaussian Mixture Models (GMM) is limited by the convergence of Expectation Maximization (EM) algorithm to local maxima. In this paper an LID system is described using Gaussian Mixture Models for the extracted features which are then trained using Split and Merge Expectation Maximization Algorithm that improves the global convergence of E...
متن کاملSMEM Algorithm for Mixture Models
We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations, we repeatedly ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition
دوره 36 شماره
صفحات -
تاریخ انتشار 2003