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.
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Image analysis with Gaussian Mixture Model (GMM), with Principal Component Analysis (PCA) for dimensionality reduction of images prior to expectation-maximization (EM) algorithm implementation.
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catherman/Image-Analysis-with-Gaussian-Mixture-Model
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Image analysis with Gaussian Mixture Model (GMM), with Principal Component Analysis (PCA) for dimensionality reduction of images prior to expectation-maximization (EM) algorithm implementation.
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