Model Selection and Parameter Estimation of One-Dimensional Gaussian Mixture Models

arXiv:2404.12613 · stat.ML, cs.LG, eess.SP, stat.ME · Submitted 2024-04-19 · Read on arXiv

stat.ML, cs.LG, eess.SP, stat.ME

Submitted: 2024-04-19

Updated: 2026-08-31

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Sample Complexity — 25× in this paper · explained in 15 episodes
Clustering — 14× in this paper · explained in 2 episodes
Accuracy — 13× in this paper · explained in 1 episode
Gaussian Mixture Model — 11× in this paper
Computational Resolution Limit — 3× in this paper
Fidelity — 2× in this paper · explained in 2 episodes
Wasserstein Distance — 2× in this paper · explained in 4 episodes
Adaptation — 1× in this paper · explained in 1 episode
Akaike Information Criterion — 1× in this paper
Bayesian Information Criterion — 1× in this paper
Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
Generalization — 1× in this paper · explained in 13 episodes
Inverse Problem — 1× in this paper · explained in 2 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Quantization — 1× in this paper · explained in 12 episodes
Robustness — 1× in this paper · explained in 5 episodes
Signal-to-Noise Ratio — 1× in this paper
Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
Singular Value Decomposition — 1× in this paper
Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming

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