高维项目因子分析的生成对抗网络:一个深度对抗学习算法
1Department of Applied Psychology and Human Development, https://ror.org/03dbr7087University of Toronto, Canada.
Psychometrika
|November 11, 2025
概括
新的对抗方法在项目响应理论 (IRT) 中改进了项目因子分析 (IFA). 对抗变量贝叶斯 (AVB) 和其扩展 (IWAVB) 为复杂的数据提供了更大的灵活性和准确性,优于标准模型.
科学领域:
- 心理测量 心理测量 心理测量
- 机器学习 机器学习
- 统计建模 统计建模
背景情况:
- 深度学习的进步已经在项目响应理论 (IRT) 中改进了项目因子分析 (IFA).
- 变化自编码器 (VAE) 是IFA中高维潜变量常见的,但它们的推理网络有局限性.
- 在VAE推理网络中,有限的表达力可能会阻碍IFA中的表现.
研究的目的:
- 介绍Adversarial Variational Bayes (AVB) 和重要性加权的AVB (IWAVB) 作为IFA的高级推理算法.
- 在IFA中提高潜变量建模的灵活性和性能.
- 能够将IFA扩展到复杂,大规模和多式联运数据集.
主要方法:
- 将VAE与生成对抗网络 (GAN) 结合起来,以创建AVB.
- 在AVB中利用辅助区分器网络将估计框架作为两人游戏.
- 开发了一个重要度加权扩展 (IWAVB) 以提高灵活性和概率估计.
主要成果:
- 理论上,AVB和IWAVB的概率与VAE和重要度加权自动编码器 (IWAE) 相匹配或超过.
- 经验数据分析显示,IWAVB比IWAE实现了更高的概率,表明更大的表达力.
- 与IWAE相比,模拟显示了IWAVB与IWAE相比,具有可比的参数恢复和优越的性能,具有多式联络潜伏分布.
结论:
- IWAVB为项目因子分析提供了更具表达性和灵活性的推理方法.
- 提出的方法促进了心理测量与现代多式联络数据分析的整合.
- IWAVB对将IFA应用于复杂,大规模和多式联网数据设置具有前景.
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