探索变化自编码器在心理数据中建模非线性关系的潜力
Nicola Milano1, Monica Casella1, Raffaella Esposito1
1Natural and Artificial Cognition Laboratory "Orazio Miglino", Department of Humanistic Studies, University of Naples Federico II, 80133 Naples, Italy.
Behavioral sciences (Basel, Switzerland)
|July 27, 2024
概括
变量自编码器为探索心理数据提供了传统因子分析的强大替代方案. 这些人工神经网络可以揭示复杂的非线性关系,并提高因子得分的准确性,特别是当传统方法不足时.
科学领域:
- 心理测量 心理测量 心理测量
- 机器学习 机器学习
- 计算统计学 计算统计学
背景情况:
- 因子分析是心理测量研究的基石,用于理解潜在的变量结构.
- 像因子分析这样的传统方法假设线性关系,并且可能会在复杂的心理数据中失败.
- 人工神经网络为探索超越线性假设的潜在空间提供了一个有希望的途径.
研究的目的:
- 调查变量自编码器 (VAE) 在心理测量研究中的非线性维度减少的实用性.
- 在建模项目-因素关系时,将VAE与传统因素分析的性能进行比较.
- 评估VAE在模拟和现实世界的心理数据中处理线性和非线性关联的能力.
主要方法:
- 使用一个变化自编码器 (VAE),一种人工神经网络,用于非线性维度缩小.
- 将VAE应用于已知线性和非线性项目-因素关系的模拟数据集.
- 通过模拟和真实数据集对因素分析进行VAE绩效评估.
主要成果:
- 在线性情景中,VAE的表现与因子分析相当,并且成功地复制了因子得分.
- 与因子分析不同,VAE有效地模拟了观察到的变量和潜在因素之间的非线性关系.
- 来自VAE的因子评分估计比因子分析的估计更准确,特别是在非线性条件下.
结论:
- 变量自编码器在心理测量分析中提供了一种强大的非线性维度减小方法.
- 与传统的因子分析相比,VAE提供了更高的准确性和灵活性,特别是对于复杂的心理数据结构.
- 这些发现突出了VAE在心理数据中发现复杂关系的潜力,使用较少的假设.
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