论文标题
高斯混合物模型参数估计的新推导：基于mm的方法
New Derivation for Gaussian Mixture Model Parameter Estimation: MM Based Approach
论文作者
论文摘要
在这封信中，我们重新审视了高斯混合模型（GMM）参数最大似然估计（MLE）的问题，并显示其参数的新推导。与采用预期最大化技术（EM）技术的经典方法不同，新的派生是直接的，并且不会调用任何隐藏或潜在变量，也没有调用条件密度函数的计算。新的推导基于少量最大化的方法，涉及找到对数似然标准的更紧密的下限。通过新派生获得的参数的更新步骤与通过经典EM算法获得的更新步骤相同。
In this letter, we revisit the problem of maximum likelihood estimation (MLE) of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its parameters. The new derivation, unlike the classical approach employing the technique of expectation-maximization (EM), is straightforward and doesn't invoke any hidden or latent variables and calculation of the conditional density function. The new derivation is based on the approach of minorization-maximization and involves finding a tighter lower bound of the log-likelihood criterion. The update steps of the parameters, obtained via the new derivation, are same as the update steps obtained via the classical EM algorithm.
