扭曲密度比率估计方法
Zi-En Fan1, Feng Lian1, Xin-Ran Li1
1School of Automation Science and Technology, Xi'an Jiaotong University, No.28, West Xianning Road, Xi'an, 710049, Shaanxi, China.
概括
扭曲密度比率估计 (WDRE) 使用规范化流量来估计概率密度比率. 这种新的方法通过转换分布来简化复杂比率估计,提高机器学习任务的准确性.
科学领域:
- 机器学习 机器学习
- 统计推理 统计推理
- 可能性理论概率理论.
背景情况:
- 估计概率密度比率 (p/q) 在机器学习中至关重要.
- 基于标准分类的方法在有很大的差异的分布上扎.
- 现有的中间分销策略复杂,缺乏明确的准确性指导方针.
研究的目的:
- 提出一种新的方法,即扭曲密度比率估计 (WDRE),用于准确估计密度比率.
- 在处理高差异分布时解决当前方法的局限性.
- 为密度比率估计提供更有效,更准确的方法.
主要方法:
- 开发了使用规范化流量的扭曲密度比率估计 (WDRE).
- 将原始密度比重重构成两个更简单的比率的乘积,减少差异.
- 应用了转换来推动分布向分析可处理的目标.
主要成果:
- WDRE有效地估计了密度比率,即使对于有很大的差异的分布.
- 该方法避免了多个中间分布的需要.
- 通过在数据和潜伏空间中适用,WDRE表现出灵活性.
结论:
- 在密度比率估计方面,WDRE提供了显著的进步.
- 基于规范流量的方法提高了准确性,并简化了估计过程.
- 实验结果验证了WDRE在合成和现实数据集上的有效性.
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