权衡的对比学习与硬负采矿用于积极和未标记的学习
概括
本研究介绍了加权对比学习与积极和未标记的学习 (WConPU) 的硬负挖矿. WConPU提高了数据表示质量,克服了当前方法的局限性,提高了分类器的性能.
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:
- 积极和未标记 (PU) 学习只使用积极和未标记数据来训练分类器.
- 当前的PU学习方法通常将未标记的数据视为负面数据,导致数据表示和性能差.
- 现有的方法难以生成高质量的数据表示,导致负预测偏差和精度降低.
研究的目的:
- 提出一种新的算法,加权对比学习与积极和未标记学习 (WConPU) 的硬负挖矿,以解决PU学习的局限性.
- 提高数据表示的质量,以提高PU学习性能.
- 使用预期最大化 (EM) 算法框架,为拟议的方法提供理论上的理由.
主要方法:
- 开发了一个新的算法,WConPU,集成一个对比学习 (CL) 模块和一个分类器训练模块.
- 设计了一种新的原型对比策略,用于PU学习中的歧视性表示学习.
- 引入了加权对比的目标功能,并采用了基于原型的硬负挖掘模块,以提高表示质量.
主要成果:
- 拟议的WConPU算法反复地提高了表示质量和分类器性能.
- 理论分析证明了该方法与预期最大化 (EM) 算法的对齐.
- 对各种基准数据集的实证评估表明,WConPU的表现优于最先进的PU学习方法.
结论:
- 对于PU学习任务,WConPU有效地产生了歧视性表示.
- 该算法克服了现有方法中出现的负预测偏好和性能下降.
- WConPU在积极和未标记的学习方法方面取得了重大进展.
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