arXiv paper analyzes self-training for linear classifiers

By PulseAugur Editorial · [1 sources] · 2026-07-27 04:00

A new research paper delves into the mechanics of self-training for linear classifiers in high-dimensional Gaussian mixture data. The study, which analyzes the asymptotic behavior of iterative self-training, reveals how the method improves generalization through different mechanisms depending on the number of iterations. Researchers propose two heuristics to address performance degradation in the presence of label imbalance, aiming to match supervised learning outcomes. AI

IMPACT Provides theoretical insights into semi-supervised learning techniques.

RANK_REASON Academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv paper analyzes self-training for linear classifiers

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arXiv stat.ML TIER_1 English(EN) · Takashi Takahashi · 2026-07-27 04:00

The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data

arXiv:2205.07739v4 Announce Type: replace Abstract: Self-training (ST) is a simple yet effective semi-supervised learning method. However, why and how ST improves generalization performance by using potentially erroneous pseudo-labels is still not well understood. To deepen the u…

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The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data

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