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Solving Inverse Problems with Piecewise Linear
- A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm.
opencv em算法
- Expectation-Maximization The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in a form of the Gaussian mixture distribution with a specified number of mixtures.
GMM
- Source code - create Gaussian Mixture Model in following steps: 1, K-means 2, Expectation-Maxximization 3, GMM Notice: All datapoints are generated randomly and you can config in Config.h-Source code- create Gaussian Mixture Model
EM
- EM algorithm for adaptive multivariate Gaussian mixtures
lect26-em
- EM algorithm for adaptive multivariate Gaussian mixtures
Lecture13
- EM algorithm for adaptive multivariate Gaussian mixtures
gmm_em_graphcut
- EM algorithm for adaptive multivariate Gaussian mixtures
lec12
- EM algorithm for adaptive multivariate Gaussian mixtures
Documentation-fixed
- EM algorithm for adaptive multivariate Gaussian mixtures
Gupta-and-Chen---2010---Theory
- This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs),
EM
- 实验报告，实现：对于混合高斯分布的情况，使用最大期望算法，通过不断计算每个样本的均值与方差，使得似然函数达到最大值。可以很好地处理满足一定概率分布的数据。 代码中通过mvnrnd()函数，设定其中的参数，产生符合混合高斯分布的一组数据集。-Lab reports, to achieve: the case of the mixed Gaussian distribution, using expectation-maximization algorithm, through continuo