I am interested in parametric and non-parametric machine learning algorithms, their advantages and disadvantages and also their main differences regarding computational complexities. In particular I am interested in the parametric Gaussian Mixture Model (GMM) and the non-parametric kernel density estimation (KDE). As I understood it that if a "small" number of data points is used then parametric (like GMM/EM) are the better choice but if the amount of data points increases to a much higher number then non-parametric algorithms are better. Could someone please explain both in bit more detail regarding comparison?
computer-vision
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