# QDA vs EM with Gaussian likelihoods

QDA (quadratic discriminant analysis) assumes that the K different classes are generated by K different multivariate Gaussians, each with potentially different mean vector and covariance matrix.

If these assumptions hold, will QDA give the same classification results as using EM (expectation maximization) clustering with Gaussian likelihoods?