Tag: gaussian-mixture-models

• Unsupervised Learning of Gaussian Mixture Models With the EM Algorithm (13 Dec 2019)
  The Expectation-Maximization algorithm is a powerful iterative method for calculating maximum likelihood estimates from unlabeled data. In this article, we will be exploring its use in Gaussian mixture models to perform the task of clustering with the Scikit-learn digits dataset. The performance of the EM-algorithm is then compared to a vanilla k-Means implementation provided in Scikit-learn.