100 citations · 268 across the 20 of their papers we have counts for
5 papers · 1 filter
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns
Takanori Fujiwara, Yun-Hsin Kuo, Anders Ynnerman +1
Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data. One main aim of DR is to reveal hidden patterns that lie on intrinsic low-dimensio…
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data
Yun-Hsin Kuo, Takanori Fujiwara, Charles C. -K. Chou +2
Analyzing air pollution data is challenging as there are various analysis focuses from different aspects: feature (what), space (where), and time (when). As in most geospatial anal…
Interactive Dimensionality Reduction for Comparative Analysis
Takanori Fujiwara, Xinhai Wei, Jian Zhao +1
Finding the similarities and differences between groups of datasets is a fundamental analysis task. For high-dimensional data, dimensionality reduction (DR) methods are often used…
Network Comparison with Interpretable Contrastive Network Representation Learning
Takanori Fujiwara, Jian Zhao, Francine Chen +2
Identifying unique characteristics in a network through comparison with another network is an essential network analysis task. For example, with networks of protein interactions ob…
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning
Takanori Fujiwara, Oh-Hyun Kwon, Kwan-Liu Ma
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 re…