Visualization of Humansʼ Subjectivity with Human-in-the-loop Dimensionality Reduction

This study proposes a Human-in-the-loop dimensionality reduction method for visualizing human subjectivity on two-dimensional maps. Unlike other dimensionality reduction methods, this study involves humans in the algorithm, allowing them to provide subjective characteristics that are difficult to formulate mathematically. The characteristics are then used to build a personalized and subjective map. The objective of the proposed idea is not to build a new dimensionality reduction method that outperforms existing methods in dimensionality reduction criteria. The novelty of this study lies in human involvement in the visualization of high-dimensional data. As a case study in this research, we generate customized maps reflecting humans’ preferences for various snacks.