Clustering a Mixture of Gaussians with Unknown Covariance

• URL: http://arxiv.org/abs/2110.01602v1
• Date: Mon, 4 Oct 2021 17:59:20 GMT
• Title: Clustering a Mixture of Gaussians with Unknown Covariance
• Authors: Damek Davis, Mateo Diaz, Kaizheng Wang
• Abstract summary: We derive a Max-Cut integer program based on maximum likelihood estimation. We develop an efficient spectral algorithm that attains the optimal rate but requires a quadratic sample size. We generalize the Max-Cut program to a $k$-means program that handles multi-component mixtures with possibly unequal weights.
• Score: 4.821312633849745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate a clustering problem with data from a mixture of Gaussians that share a common but unknown, and potentially ill-conditioned, covariance matrix. We start by considering Gaussian mixtures with two equally-sized components and derive a Max-Cut integer program based on maximum likelihood estimation. We prove its solutions achieve the optimal misclassification rate when the number of samples grows linearly in the dimension, up to a logarithmic factor. However, solving the Max-cut problem appears to be computationally intractable. To overcome this, we develop an efficient spectral algorithm that attains the optimal rate but requires a quadratic sample size. Although this sample complexity is worse than that of the Max-cut problem, we conjecture that no polynomial-time method can perform better. Furthermore, we gather numerical and theoretical evidence that supports the existence of a statistical-computational gap. Finally, we generalize the Max-Cut program to a $k$-means program that handles multi-component mixtures with possibly unequal weights. It enjoys similar optimality guarantees for mixtures of distributions that satisfy a transportation-cost inequality, encompassing Gaussian and strongly log-concave distributions.

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