Using Penalized Regression with Parallel Coordinates for Visualization of Significance in High Dimensional Data

In recent years, there has been an exponential increase in the amount of data being produced and disseminated by diverse applications, intensifying the need for the development of effective methods for the interactive visual and analytical exploration of large, high-dimensional datasets. In this paper, we describe the development of a novel tool for multivariate data visualization and exploration based on the integrated use of regression analysis and advanced parallel coordinates visualization. Conventional parallel-coordinates visualization is a classical method for presenting raw multivariate data on a 2D screen. However, current tools suffer from a variety of problems when applied to massively high-dimensional datasets. Our system tackles these issues through the combined use of regression analysis and a variety of enhancements to traditional parallel-coordinates display capabilities, including new techniques to handle visual clutter, and intuitive solutions for selecting, ordering, and grouping dimensions. We demonstrate the effectiveness of our system through two case-studies.

@article{Wang2013,
  title     = {Using Penalized Regression with Parallel Coordinates for Visualization of Significance in High Dimensional Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {10},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Shengwen Wang and Yi Yang and Jih-Sheng Chang and Fang-Pang Lin},
  doi       = {10.14569/IJACSA.2013.041006},
  url       = {https://doi.org/10.14569/IJACSA.2013.041006}
}