Visual Computing

University of Konstanz
IEEE Transactions on Visualization and Computer Graphics

Data Sampling in Multi-view and Multi-class Scatterplots via Set Cover Optimization

R. Hu, T. Sha, O. Van Kaick, O. Deussen, H. Huang
Teaser of Data Sampling in Multi-view and Multi-class Scatterplots via Set Cover Optimization

Material

Paper (.pdf, 11.0 MB)

Abstract

We present a method for data sampling in scatterplots by jointly optimizing point selection for different views or classes. Our method uses space-filling curves (Z-order curves) that partition a point set into subsets that, when covered each by one sample, provide a sampling or coreset with good approximation guarantees in relation to the original point set. For scatterplot matrices with multiple views, different views provide different space-filling curves, leading to different partitions of the given point set. For multi-class scatterplots, the focus on either per-class distribution or global distribution provides two different partitions of the given point set that need to be considered in the selection of the coreset. For both cases, we convert the coreset selection problem into an Exact Cover Problem (ECP), and demonstrate with quantitative and qualitative evaluations that an approximate solution that solves the ECP efficiently is able to provide high-quality samplings.

BibTeX

@article{Hu2019DataSamplingMulti,
  author     = {R. Hu and T. Sha and O. Van Kaick and O. Deussen and H. Huang},
  doi        = {10.1109/TVCG.2019.2934799},
  issn       = {1077-2626},
  journal    = {IEEE Transactions on Visualization and Computer Graphics},
  title      = {Data Sampling in Multi-view and Multi-class Scatterplots via Set Cover Optimization},
  year       = {2019},
  url        = {http://graphics.uni-konstanz.de/publikationen/Hu2019DataSamplingMulti},
}