It'd be nice if vector based approaches (think MBTI tests being originally created using PCA) would use state of the art dimensionality reduction techniques instead of stuff from the 70s so that way each vector actually matters and explains far more variance in the data.
If you need interpretability just use an autoencoder or simese network instead of PCA. This way the new "personality" vectors are highly meaningful and visualizations are better...
If you need interpretability just use an autoencoder or simese network instead of PCA. This way the new "personality" vectors are highly meaningful and visualizations are better...