A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction

@article{Fujiwara2021AVA,
  title={A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction},
  author={Takanori Fujiwara and Shilpika and Naohisa Sakamoto and Jorji Nonaka and Keiji Yamamoto and Kwan-Liu Ma},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2021},
  volume={27},
  pages={1601-1611}
}
  • T. Fujiwara, Shilpika, +3 authors K. Ma
  • Published 2 August 2020
  • Computer Science, Medicine
  • IEEE Transactions on Visualization and Computer Graphics
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. However, DR is usually applied to a subset of data that is either single-time-point multivariate or univariate time-series, resulting in the need to manually examine and correlate the DR results out of different data subsets. When the number of dimensions is… 
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