1 code implementation • 16 Mar 2023 • Hsiao-Ying Lu, Takanori Fujiwara, Ming-Yi Chang, Yang-chih Fu, Anders Ynnerman, Kwan-Liu Ma
Multivariate networks are commonly found in real-world data-driven applications.
no code implementations • 6 Mar 2023 • Yiran Li, Junpeng Wang, Takanori Fujiwara, Kwan-Liu Ma
Adversarial attacks on a convolutional neural network (CNN) -- injecting human-imperceptible perturbations into an input image -- could fool a high-performance CNN into making incorrect predictions.
no code implementations • 28 Jun 2022 • Takanori Fujiwara, Yun-Hsin Kuo, Anders Ynnerman, Kwan-Liu Ma
Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data.
no code implementations • 11 Feb 2022 • Yun-Hsin Kuo, Takanori Fujiwara, Charles C. -K. Chou, Chun-houh Chen, Kwan-Liu Ma
In this paper, we present a methodology that utilizes multiple machine learning methods to uniformly explore these aspects.
1 code implementation • 29 Jun 2021 • Takanori Fujiwara, Xinhai Wei, Jian Zhao, Kwan-Liu Ma
However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups.
no code implementations • 13 Oct 2020 • Chaoqing Xu, Tyson Neuroth, Takanori Fujiwara, Ronghua Liang, Kwan-Liu Ma
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 diseases as well as a better understanding of how they affect the brain.
no code implementations • 2 Aug 2020 • Takanori Fujiwara, Shilpika, Naohisa Sakamoto, Jorji Nonaka, Keiji Yamamoto, Kwan-Liu Ma
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 uncover the intrinsic structure and features of the data.
no code implementations • 1 Aug 2020 • Takanori Fujiwara, Jian Zhao, Francine Chen, Kwan-Liu Ma
A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other.
1 code implementation • 9 Jul 2020 • Takanori Fujiwara, Tzu-Ping Liu
Scaling methods have long been utilized to simplify and cluster high-dimensional data.
3 code implementations • 25 May 2020 • Takanori Fujiwara, Jian Zhao, Francine Chen, Yao-Liang Yu, Kwan-Liu Ma
This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another.
no code implementations • 18 Feb 2020 • Rongchen Guo, Takanori Fujiwara, Yiran Li, Kelly M. Lima, Soman Sen, Nam K. Tran, Kwan-Liu Ma
While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding.
no code implementations • 18 Feb 2020 • Yiran Li, Takanori Fujiwara, Yong K. Choi, Katherine K. Kim, Kwan-Liu Ma
Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
1 code implementation • 26 Jan 2020 • Suraj P. Kesavan, Takanori Fujiwara, Jianping Kelvin Li, Caitlin Ross, Misbah Mubarak, Christopher D. Carothers, Robert B. Ross, Kwan-Liu Ma
To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization.
no code implementations • 10 May 2019 • Takanori Fujiwara, Jia-Kai Chou, Shilpika, Panpan Xu, Liu Ren, Kwan-Liu Ma
We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.
no code implementations • 10 May 2019 • 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.