On the Role of Channel Capacity in Learning Gaussian Mixture Models

Elad Romanov, Tamir Bendory, Or Ordentlich

Research output: Contribution to journalConference articlepeer-review


This paper studies the sample complexity of learning the k unknown centers of a balanced Gaussian mixture model (GMM) in Rd with spherical covariance matrix σ2I. In particular, we are interested in the following question: what is the maximal noise level σ2, for which the sample complexity is essentially the same as when estimating the centers from labeled measurements? To that end, we restrict attention to a Bayesian formulation of the problem, where the centers are uniformly distributed on the sphere dSd−1. Our main results characterize the exact noise threshold σ2 below which the GMM learning problem, in the large system limit d, k → ∞, is as easy as learning from labeled observations, and above which it is substantially harder. The threshold occurs at log k = 12 log (1 + σ12 ), which is the capacity of the additive white Gaussian noise (AWGN) chand nel. Thinking of the set of k centers as a code, this noise threshold can be interpreted as the largest noise level for which the error probability of the code over the AWGN channel is small. Previous works on the GMM learning problem have identified the minimum distance between the centers as a key parameter in determining the statistical difficulty of learning the corresponding GMM. While our results are only proved for GMMs whose centers are uniformly distributed over the sphere, they hint that perhaps it is the decoding error probability associated with the center constellation as a channel code that determines the statistical difficulty of learning the corresponding GMM, rather than just the minimum distance.

Original languageAmerican English
Pages (from-to)4110-4159
Number of pages50
JournalProceedings of Machine Learning Research
StatePublished - 2022
Event35th Conference on Learning Theory, COLT 2022 - London, United Kingdom
Duration: 2 Jul 20225 Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 E. Romanov, T. Bendory & O. Ordentlich.


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