Image-Analysis-with-Gaussian-Mixture-Model

In this analysis, we first use Principal Component Analysis (PCA) to reduce the dimensionality of the images. We then implement the expectation-maximization (EM) algorithm to fit a Gaussian mixture model (GMM) with the MNIST handwritten digits dataset. These results are compared to analysis using k-means clustering.