Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning

• Published in: IEEE transactions on visualization and computer graphics Vol. 26; no. 1; pp. 45 - 55
• Main Authors: Fujiwara, Takanori, Kwon, Oh-Hyun, Ma, Kwan-Liu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; contrastive learning; Data visualization; Dimensionality reduction; Feature extraction; high-dimensional data; Identification methods; Interactive systems; Methods; Principal component analysis; Principal components analysis; Reduction; Task analysis; Visual analytics

Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data. However, to interpret the DR result for gaining useful insights from the data, it would take additional analysis effort such as identifying clusters an...