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

2022-05-16 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Takashi Takahashi's analysis characterizes iterative self-training (ST) behavior for linear classifiers on high-dimensional binary Gaussian mixtures. The study, submitted on May 16, 2022, and last revised July 24, 2026, investigates how ST improves generalization performance despite using potentially erroneous pseudo-labels. In the asymptotic limit where input dimension and data size diverge proportionally, ST's effectiveness varies with iteration count. Early iterations improve generalization by fitting reliable pseudo-labels with large parameter updates. Conversely, many iterations incrementally refine the classification plane direction using soft labels and small regularization, extracting information almost noiselessly. The research also identifies that ST underperforms supervised learning when label imbalance is present, proposing two heuristics to mitigate this issue and achieve comparable performance.

Key takeaway

For Machine Learning Engineers deploying semi-supervised learning, understanding self-training's iterative dynamics is crucial. You should tailor your ST iteration strategy, as early iterations fit reliable pseudo-labels while many iterations incrementally refine the classification plane. If your datasets exhibit label imbalance, implement the proposed heuristics to ensure ST performance remains competitive with supervised learning, preventing generalization degradation.

Key insights

Self-training's generalization benefits depend on iteration count and can be improved for label imbalance.

Principles

• ST improves generalization via distinct mechanisms.
• Small ST iterations fit relatively reliable pseudo-labels.
• Many ST iterations refine classification plane direction incrementally.

Method

The paper derives and analyzes a sharp characterization of iterative self-training behavior for linear classifiers on binary Gaussian mixtures in an asymptotic limit.

In practice

• Consider iteration count for ST performance.
• Apply proposed heuristics for label imbalance.
• Use soft labels for incremental updates.

Topics

• Self-training
• Pseudo-labeling
• Linear Classifiers
• Gaussian Mixture Models
• Semi-supervised Learning
• Label Imbalance

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.