论文标题
对比性多重对应分析（CMCA）：使用对比度学习来识别政党中的潜在亚组
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties
论文作者
论文摘要
长期以来，扩展方法已被用来简化和群集高维数据。但是，从这些方法得出的所有预定义组中的一般潜在空间有时不会落入研究人员对组内特定模式的兴趣。为了解决这个问题，我们采用了一种新兴分析方法，称为对比度学习。我们通过将其思想扩展到多个对应分析（MCA），以实现对社会科学家经常遇到的数据的分析（包含二进制，序数和名义变量），从而为这一不断增长的领域做出了贡献。我们通过分析美国和英国的两种不同选民的调查来证明对比度MCA（CMCA）的实用性。我们的结果表明，首先，CMCA可以确定传统方法忽略的亚组之间实质上重要的维度和分裂；其次，在其他情况下，CMCA可以得出潜在特征，这些特征强调在传统方法中中等程度上看到的亚组。
Scaling methods have long been utilized to simplify and cluster high-dimensional data. However, the general latent spaces across all predefined groups derived from these methods sometimes do not fall into researchers' interest regarding specific patterns within groups. To tackle this issue, we adopt an emerging analysis approach called contrastive learning. We contribute to this growing field by extending its ideas to multiple correspondence analysis (MCA) in order to enable an analysis of data often encountered by social scientists -- containing binary, ordinal, and nominal variables. We demonstrate the utility of contrastive MCA (cMCA) by analyzing two different surveys of voters in the U.S. and U.K. Our results suggest that, first, cMCA can identify substantively important dimensions and divisions among subgroups that are overlooked by traditional methods; second, for other cases, cMCA can derive latent traits that emphasize subgroups seen moderately in those derived by traditional methods.
