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  • 1.
    Fujiwara, Takanori
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Liu, Tzu-Ping
    Univ Taipei, Taiwan.
    Contrastive multiple correspondence analysis (cMCA): Using contrastive learning to identify latent subgroups in political parties2023In: PLOS ONE, E-ISSN 1932-6203, Vol. 18, no 7Article in journal (Refereed)
    Abstract [en]

    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 sometimes do not fall into researchers interest regarding specific patterns within groups. To tackle this issue, we adopt an emerging analysis approach called contrastive learning. We contribute to this growing field by extending its ideas to multiple correspondence analysis (MCA) in order to enable an analysis of data often encountered by social scientists-containing binary, ordinal, and nominal variables. We demonstrate the utility of contrastive MCA (cMCA) by analyzing two different surveys of voters in the U.S. and U.K. Our results suggest that, first, cMCA can identify substantively important dimensions and divisions among subgroups that are overlooked by traditional methods; second, for other cases, cMCA can derive latent traits that emphasize subgroups seen moderately in those derived by traditional methods.

  • 2.
    Fujiwara, Takanori
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Kuo, Yun-Hsin
    Univ Calif Davis, CA USA.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ma, Kwan-Liu
    Univ Calif Davis, CA USA.
    Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns2023In: 2023 IEEE 16TH PACIFIC VISUALIZATION SYMPOSIUM, PACIFICVIS, IEEE COMPUTER SOC , 2023, p. 122-131Conference paper (Refereed)
    Abstract [en]

    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-dimensional manifolds. However, DR often overlooks important patterns when the manifolds are distorted or masked by certain influential data attributes. This paper presents a feature learning framework, FEALM, designed to generate a set of optimized data projections for nonlinear DR in order to capture important patterns in the hidden manifolds. These projections produce maximally different nearest-neighbor graphs so that resultant DR outcomes are significantly different. To achieve such a capability, we design an optimization algorithm as well as introduce a new graph dissimilarity measure, named neighbor-shape dissimilarity. Additionally, we develop interactive visualizations to assist comparison of obtained DR results and interpretation of each DR result. We demonstrate FEALMs effectiveness through experiments and case studies using synthetic and real-world datasets.

  • 3.
    Li, Yiran
    et al.
    Univ Calif Davis, CA 95616 USA.
    Wang, Junpeng
    Visa Res, CA 94306 USA.
    Fujiwara, Takanori
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ma, Kwan-Liu
    Univ Calif Davis, CA 95616 USA.
    Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks2023In: ACM Transactions on Interactive Intelligent Systems, ISSN 2160-6455, E-ISSN 2160-6463, Vol. 13, no 4, article id 20Article in journal (Refereed)
    Abstract [en]

    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. The success of adversarial attacks raises serious concerns about the robustness of CNNs, and prevents them from being used in safety-critical applications, such asmedical diagnosis and autonomous driving. Ourwork introduces a visual analytics approach to understanding adversarial attacks by answering two questions: (1) Which neurons are more vulnerable to attacks? and (2) Which image features do these vulnerable neurons capture during the prediction? For the first question, we introduce multiple perturbation-based measures to break down the attacking magnitude into individual CNN neurons and rank the neurons by their vulnerability levels. For the second, we identify image features (e.g., cat ears) that highly stimulate a user-selected neuron to augment and validate the neuron's responsibility. Furthermore, we support an interactive exploration of a large number of neurons by aiding with hierarchical clustering based on the neurons' roles in the prediction. To this end, a visual analytics system is designed to incorporate visual reasoning for interpreting adversarial attacks. We validate the effectiveness of our system through multiple case studies aswell as feedback from domain experts.

  • 4.
    Fujita, Keijiro
    et al.
    Kobe University.
    Sakamoto, Naohisa
    Kobe University.
    Fujiwara, Takanori
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Tsukamoto, Toshiyuki
    RIKEN R-CCS.
    Nonaka, Jorji
    RIKEN R-CCS.
    A Visual Analytics Method for Time-Series Log Data Using Multiple Dimensionality Reduction2022In: Journal of Advanced Simulation in Science and Engineering, E-ISSN 2188-5303, Vol. 9, no 2, p. 206-219Article in journal (Refereed)
    Abstract [en]

    The size and complexity of supercomputer systems and their power and cooling facilities have continuously increased, thus posing additional challenge for long-term and stable operation. Supercomputers are shared computational resources and usually operate with different computational workloads at different locations (space) and timings (time). Better understanding of the supercomputer systems heat generation and cooling behavior is highly desired from the facility operational side for decision making and optimization planning. In this work, we present a dimensionality reduction-based visual analytics method for time-series log data, from supercomputer system and its facility, to capture characteristic spatio-temporal features and behaviors during the operation.

  • 5.
    Fujiwara, Takanori
    et al.
    University of California, Davis, United States.
    Zhao, Jian
    University of Waterloo, Canada.
    Chen, Francine
    Toyota Research Institute.
    Yu, Yaoliang
    University of Waterloo, Canada.
    Ma, Kwan-Liu
    University of California, Davis, United States.
    Network Comparison with Interpretable Contrastive Network Representation Learning2022In: Journal of Data Science, Statistics, and Visualisation, ISSN 2773-0689, Vol. 2, no 5Article in journal (Refereed)
    Abstract [en]

    Identifying unique characteristics in a network through comparison with another network is an essential network analysis task. For example, with networks of protein interactions obtained from normal and cancer tissues, we can discover unique types of interactions in cancer tissues. 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. However, existing contrastive learning methods cannot be directly applied to networks as they are designed only for high-dimensional data analysis. To address this problem, we introduce a new analysis approach called contrastive network representation learning (cNRL). By integrating two machine learning schemes, network representation learning and contrastive learning, cNRL enables embedding of network nodes into a low-dimensional representation that reveals the uniqueness of one network compared to another. Within this approach, we also design a method, named i-cNRL, which offers interpretability in the learned results, allowing for understanding which specific patterns are only found in one network. We demonstrate the effectiveness of i-cNRL for network comparison with multiple network models and real-world datasets. Furthermore, we compare i-cNRL and other potential cNRL algorithm designs through quantitative and qualitative evaluations.

  • 6.
    Fujita, Keijiro
    et al.
    Kobe University, Japan.
    Sakamoto, Naohisa
    Kobe University, Japan.
    Fujiwara, Takanori
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Nonaka, Jorji
    RIKEN R-CCS, Japan.
    Tsukamoto, Toshiyuki
    RIKEN R-CCS, Japan.
    次元削減技術を用いた視覚的テンソルデータ解析2022Report (Other academic)
    Abstract [ja]

    多次元時系列データから,そこに内在する特徴構造を抽出し解釈するためのデータ解析手法に対する要求が高まっている.本研究では、解析対象とするデータを時間・空間・変数を軸(モード)とするテンソルデータとして表現し,多段階次元削減技術を応用することで,特徴構造を効果的に視覚化し,対話的にデータ探索を行うことができる視覚的解析手法を開発する.開発した手法を,実世界上で計測された時系列データ(スパコンログデータなど)に適用し,その有効性を検証する.

  • 7.
    Fujiwara, Takanori
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Kuo, Yun-Hsin
    University of California, Davis.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ma, Kwan-Liu
    University of California, Davis.
    Feature Learning for Dimensionality Reduction toward Maximal Extraction of Hidden PatternsManuscript (preprint) (Other academic)
    Abstract [en]

    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-dimensional manifolds. However, DR often overlooks important patterns when the manifolds are strongly distorted or hidden by certain influential data attributes. This paper presents a feature learning framework, FEALM, designed to generate an optimized set of data projections for nonlinear DR in order to capture important patterns in the hidden manifolds. These projections produce maximally different nearest-neighbor graphs so that resultant DR outcomes are significantly different. To achieve such a capability, we design an optimization algorithm as well as introduce a new graph dissimilarity measure, called neighbor-shape dissimilarity. Additionally, we develop interactive visualizations to assist comparison of obtained DR results and interpretation of each DR result. We demonstrate FEALM's effectiveness through experiments using synthetic datasets and multiple case studies on real-world datasets.

1 - 7 of 7
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