In this article, we discuss how Visualization (VIS) with Machine Learning (ML) could mutually benefit from each other. We do so through the lens of our own experience working at this intersection for the last decade. Particularly we focus on describing how VIS supports explaining ML models and aids ML-based Dimensionality Reduction techniques in solving tasks such as parameter space analysis. In the other direction, we discuss approaches showing how ML helps improve VIS, such as applying ML-based automation to improve visualization design. Based on the examples and our own perspective, we describe a number of open research challenges that we frequently encountered in our endeavors to combine ML and VIS.
%0 Journal Article
%1 NgoDennigKeimSedlmair+2022+169+180
%A Ngo, Quynh Quang
%A Dennig, Frederik L.
%A Keim, Daniel A.
%A Sedlmair, Michael
%D 2022
%J it - Information Technology
%K a03 sfbtrr161 from:christinawarren visus:ngoqh visus:sedlmaml a08 2022 visus:ngoqg visus
%N 4-5
%P 169–180
%R doi:10.1515/itit-2022-0034
%T Machine Learning Meets Visualization – Experiences and Lessons Learned
%U https://doi.org/10.1515/itit-2022-0034
%V 64
%X In this article, we discuss how Visualization (VIS) with Machine Learning (ML) could mutually benefit from each other. We do so through the lens of our own experience working at this intersection for the last decade. Particularly we focus on describing how VIS supports explaining ML models and aids ML-based Dimensionality Reduction techniques in solving tasks such as parameter space analysis. In the other direction, we discuss approaches showing how ML helps improve VIS, such as applying ML-based automation to improve visualization design. Based on the examples and our own perspective, we describe a number of open research challenges that we frequently encountered in our endeavors to combine ML and VIS.
@article{NgoDennigKeimSedlmair+2022+169+180,
abstract = {In this article, we discuss how Visualization (VIS) with Machine Learning (ML) could mutually benefit from each other. We do so through the lens of our own experience working at this intersection for the last decade. Particularly we focus on describing how VIS supports explaining ML models and aids ML-based Dimensionality Reduction techniques in solving tasks such as parameter space analysis. In the other direction, we discuss approaches showing how ML helps improve VIS, such as applying ML-based automation to improve visualization design. Based on the examples and our own perspective, we describe a number of open research challenges that we frequently encountered in our endeavors to combine ML and VIS.},
added-at = {2022-09-20T16:36:38.000+0200},
author = {Ngo, Quynh Quang and Dennig, Frederik L. and Keim, Daniel A. and Sedlmair, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c2c8035dc66c1ad4673fa9db51eddf7f/visus},
doi = {doi:10.1515/itit-2022-0034},
interhash = {b974e6a67daa08b110b91c79391608c3},
intrahash = {c2c8035dc66c1ad4673fa9db51eddf7f},
journal = {it - Information Technology},
keywords = {a03 sfbtrr161 from:christinawarren visus:ngoqh visus:sedlmaml a08 2022 visus:ngoqg visus},
lastchecked = {2022-09-20},
number = {4-5},
pages = {169–180},
timestamp = {2023-06-02T06:57:13.000+0200},
title = {Machine Learning Meets Visualization – Experiences and Lessons Learned},
url = {https://doi.org/10.1515/itit-2022-0034},
volume = 64,
year = 2022
}