Machine Learning, Clustering, Expectation Maximization, Gaussian Mixture Model, Image Clustering.
parameters (i.e. mean and variance of k Gaussian distribution) then we can know the likelihood of each data point from which Gaussian it is coming (this likelihood refers to the responsibilities). Wow, but we don’t know this either. So our conclusion is If we know the parameter (i.e. mean and