Learning Overspecified Gaussian Mixtures Exponentially Fast with the EM Algorithm
This work provides theoretical and practical insights for researchers and practitioners in machine learning, particularly in clustering and density estimation, though it is incremental as it builds on existing EM analysis with specific configurations.
The paper tackled the problem of slow convergence in the EM algorithm for overspecified Gaussian mixture models, demonstrating that under specific structural conditions, the algorithm converges exponentially fast with an O(log(1/ε)) iteration complexity to achieve ε-accuracy in KL distance, supported by numerical experiments showing dramatic acceleration.
We investigate the convergence properties of the EM algorithm when applied to overspecified Gaussian mixture models -- that is, when the number of components in the fitted model exceeds that of the true underlying distribution. Focusing on a structured configuration where the component means are positioned at the vertices of a regular simplex and the mixture weights satisfy a non-degeneracy condition, we demonstrate that the population EM algorithm converges exponentially fast in terms of the Kullback-Leibler (KL) distance. Our analysis leverages the strong convexity of the negative log-likelihood function in a neighborhood around the optimum and utilizes the Polyak-Łojasiewicz inequality to establish that an $ε$-accurate approximation is achievable in $O(\log(1/ε))$ iterations. Furthermore, we extend these results to a finite-sample setting by deriving explicit statistical convergence guarantees. Numerical experiments on synthetic datasets corroborate our theoretical findings, highlighting the dramatic acceleration in convergence compared to conventional sublinear rates. This work not only deepens the understanding of EM's behavior in overspecified settings but also offers practical insights into initialization strategies and model design for high-dimensional clustering and density estimation tasks.