A probabilistic analysis of EM for mixtures of separated, spherical Gaussians
We show that, given data from a mixture of k well-separated spherical Gaussians in R-d, a simple two-round variant of EM will, with high probability, learn the parameters of the Gaussians to near-optimal precision, if the dimension is high (d >> lnk). We relate this to previous theoretical and empirical work on the EM algorithm.
probabilistic analysis expectation maximization mixtures of gaussians clustering unsupervised learning