100 citations · 268 across the 20 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…
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…
A Visual Analytics System for Water Distribution System Optimization
Yiran Li, Erin Musabandesu, Takanori Fujiwara +2
The optimization of water distribution systems (WDSs) is vital to minimize energy costs required for their operations. A principal approach taken by researchers is identifying an o…
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…
A Visual Analytics Approach for Hardware System Monitoring with Streaming Functional Data Analysis
Fnu Shilpika, Takanori Fujiwara, Naohisa Sakamoto +2
Many real-world applications involve analyzing time-dependent phenomena, which are intrinsically functional, consisting of curves varying over a continuum (e.g., time). When analyz…
A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts
Chaoqing Xu, Tyson Neuroth, Takanori Fujiwara +2
Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these dis…
A Visual Analytics Framework for Contrastive Network Analysis
Takanori Fujiwara, Jian Zhao, Francine Chen +1
A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein intera…
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction
Takanori Fujiwara, Shilpika, Naohisa Sakamoto +3
Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to…
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties
Takanori Fujiwara, Tzu-Ping Liu
Scaling methods have long been utilized to simplify and cluster high-dimensional data. However, the general latent spaces across all predefined groups derived from these methods so…
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…
A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
Yiran Li, Takanori Fujiwara, Yong K. Choi +2
There is a growing trend of applying machine learning methods to medical datasets in order to predict patients' future status. Although some of these methods achieve high performan…
Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding
Rongchen Guo, Takanori Fujiwara, Yiran Li +4
Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under tr…
A Visual Analytics Framework for Reviewing Streaming Performance Data
Suraj P. Kesavan, Takanori Fujiwara, Jianping Kelvin Li +5
Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to va…
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
Takanori Fujiwara, Jia-Kai Chou, Shilpika +3
Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing multidimensional data. However, when data is a live streaming feed, conventional DR methods ca…
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…