49 citations · 75 across the 11 of their papers we have counts for
20 papers
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo +6
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has…
Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo +6
Dimensionality reduction (DR) techniques are essential for visually analyzing high-dimensional data. However, visual analytics using DR often face unreliability, stemming from fact…
A Computational Design Pipeline to Fabricate Sensing Network Physicalizations
S. Sandra Bae, Takanori Fujiwara, Anders Ynnerman +3
Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualizati…
Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks
Yiran Li, Junpeng Wang, Takanori Fujiwara +1
Adversarial attacks on a convolutional neural network (CNN) -- injecting human-imperceptible perturbations into an input image -- could fool a high-performance CNN into making inco…
Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction
Hsiao-Ying Lu, Takanori Fujiwara, Ming-Yi Chang +3
Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial ta…
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…