2023年6月9日 · The expectation maximization (EM) algorithm is a common method used to obtain the local optima of these parameters of probabilistic graphical models with latent variables. This chapter …

2024年3月18日 · 5. Algorithms Generally, finding the local and global optima of a function is a very important problem, and new algorithms are proposed all the time. In terms of local optima, some …

We show that there is a moderately strong relationship between a solution that has a high proportion of local optima and one that is poorly classified. Keywords: EM algorithm; Mixture modeling; local optima.

2025年7月12日 · In other words, a local optimum is a point where the function is lower (or higher) than its neighboring points. Global Optima The global optimum of a function refers to the point within its …

First of all, it is possible that EM converges to a local min, a local max, or a saddle point of the likelihood function. More precisely, as Tom Minka pointed out, EM is guaranteed to converge to a point with …

The EM algorithm is a very powerful optimization method and has become popular in many fields. Unfortunately, EM is only a local optimization method and can get stuck in sub-optimal solutions. …

2025年7月23日 · Local optima play a significant role in the field of optimization and are particularly influential in the performance and effectiveness of local search algorithms. To understand this, we …

2025年3月13日 · The EM algorithm is known to occasionally converge to local optima rather than the global maximum. This challenge can be mitigated by running multiple initializations, known as “multi …

2021年10月12日 · A local optimization algorithm, also called a local search algorithm, is an algorithm intended to locate a local optima. It is suited to traversing a given region of the search space and …

2016年11月2日 · The Expectation-Maximization (EM) algorithm is an iterative method that is often used for parameter estimation in incomplete data problems. Despite much theoretical endeavors devoted …