可扩展的半监督学习与歧视性标签传播和纠正
IEEE transactions on pattern analysis and machine intelligence
|January 19, 2026
概括
本研究介绍了歧视性标签传播和纠正 (DLPC),这是一个新的半监督学习框架. DLPC有效地结合了回归损失和相似性结构,改善了边界样本上的模型性能,并提高了可扩展性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 半监督学习利用了标记和未标记的数据,但面临着现有方法的挑战.
- 当前的方法往往侧重于相似性结构或回归损失,忽视它们的相互作用.
- 边界样本之间不可靠的相似性结构可能会误导标签的传播,并损害样本之外的性能.
研究的目的:
- 提出一个可扩展的半监督学习框架,歧视性标签传播和纠正 (DLPC),解决现有方法的局限性.
- 通过将它们投射到独立的类标签上来提高边界样本回归损失的有效性.
- 通过协同利用回归损失和相似性结构来提高标签质量并促进特征投影学习.
主要方法:
- DLPC通过协作利用回归损失和相似性结构.
- 样本被投射到具有非负调整向量的独立类标签上,从而扩大类间距离.
- 标签传播通过动态优化的图形结构发生,然后使用回归损失进行校正.
主要成果:
- DLPC有效地提高了标签质量,并促进了特征投影学习.
- 加速解决方案提高了计算效率,使得DLPC可扩展到大规模的问题.
- 实验表明DLPC在单视图和多视图任务中对最先进的竞争对手的有效性和优势.
结论:
- 通过整合回归损失和相似性结构,DLPC为半监督学习提供了强大且可扩展的解决方案.
- 该框架处理边界样本的能力及其计算效率使其适合各种应用.
- DLPC显示了推动半监督学习研究和实际实施的巨大潜力.
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