Sparse Contrastive Multiple Correspondence Analysis (scMCA): Making (Contrastive) Scaling Results More Interpretable

• In Event: Ideal Point Estimation

Brief Overview

We extend the approach of sparse contrastive PCA to develop scMCA, which reduces the number of selected variables for principal components so they can be more interpretable,

Authors

• Takanori Fujiwara, Linkoping University

• Tzu-Ping Liu, University of Taipei