I am studying the Gaussian Mixture model and come up with this question myself.

Suppose the underlying data is generated from a mixture of $K$ Gaussian distribution and each of them has a mean vector $\mu_k\in\mathbb{R}^p$, where $1\leq k\leq K$ and each of them has the same co-variance matrix $\Sigma$ and assume this $\Sigma$ is a diagonal matrix. And assume the mixing ratio is $1/K$, i.e., each cluster has same weight.

So in this ideal example, the only job is to estimate the $K$ mean vectors $\mu_k\in\mathbb{R}^p$, where $1\leq k\leq K$ and the co-variance matrix $\Sigma$.

My question is: if we use EM algorithm, will we be able to consistently estimate $\mu_k$ and $\Sigma$, i.e., when sample size $n\rightarrow\infty$, will the estimator produced by EM algorithm achieve the true value of $\mu_k$ and $\Sigma$?


1 Answer 1


If the algorithm is initialized with random values each time, then no, the convergence will not necessarily be consistent. Non-random initialization will presumably produce the same result every time, but I don't believe that this would necessary produce the "correct" values of $\mu_k$.

As an aside, by fixing the mixing ratio to $1/K$ and fixing $\Sigma$ to be diagonal, the algorithm becomes very similar to the $k$-means algorithm. This also has inconsistent convergence, depending on the random initialization.

  • $\begingroup$ I numerically experimented, at least for 2 independent classes of normal distribution, the EM produces consistent estimator of the class mean. However, K means cannot do that, I proved it mathematically $\endgroup$
    – KevinKim
    Commented Sep 13, 2015 at 2:39
  • 1
    $\begingroup$ Could you give more details please? E.g. what data you were using, how you initialised the parameters etc. $\endgroup$
    – dcorney
    Commented Sep 14, 2015 at 8:58
  • $\begingroup$ Agree with @dcorney. It really depends on the initial values you will choose. At least in practice wrong choice of initial values lead to unconsistent estimation (I use mixtools R package) $\endgroup$ Commented Sep 8, 2016 at 9:08

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