Fast estimation of Gaussian mixture components via centering and singular value thresholding
Estimating the number of components is a fundamental challenge in unsupervised learning, particularly when dealing with high-dimensional data with many components or severely imbalanced component sizes. This paper addresses this challenge for classical Gaussian mixture models. The proposed estimator is simple: center the data, compute the singular values of the centered matrix, and count those above a threshold. No iterative fitting, no likelihood calculation, and no prior knowledge of the number of components are required. We prove that, under a mild separation condition on the component centers, the estimator consistently recovers the true number of components. The result holds in high-...
| Authors | Huan Qing |
|---|---|
| Primary category | stat.ML |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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