Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning

Takanori Fujiwara, Oh Hyun Kwon, Kwan Liu Ma

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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 and understanding their characteristics. While there are many automatic methods (e.g., density-based clustering methods) to identify clusters, effective methods for understanding a cluster's characteristics are still lacking. A cluster can be mostly characterized by its distribution of feature values. Reviewing the original feature values is not a straightforward task when the number of features is large. To address this challenge, we present a visual analytics method that effectively highlights the essential features of a cluster in a DR result. To extract the essential features, we introduce an enhanced usage of contrastive principal component analysis (cPCA). Our method, called ccPCA (contrasting clusters in PCA), can calculate each feature's relative contribution to the contrast between one cluster and other clusters. With ccPCA, we have created an interactive system including a scalable visualization of clusters' feature contributions. We demonstrate the effectiveness of our method and system with case studies using several publicly available datasets.

Original languageEnglish (US)
Article number8805461
Pages (from-to)45-55
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume26
Issue number1
DOIs
StatePublished - Jan 2020

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Keywords

  • contrastive learning
  • Dimensionality reduction
  • high-dimensional data
  • principal component analysis
  • visual analytics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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