On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing

Fan, Chaoran; Hauser, Helwig

On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing

Files

001-005.pdf (444.83 KB)

Date

2019

Authors

Fan, Chaoran
Hauser, Helwig

Publisher

The Eurographics Association

Abstract

In this paper, we investigate to which degree the human should be involved into the model design and how good the empirical model can be with more careful design. To find out, we extended our previously published Mahalanobis brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information that is captured by the kernel density estimation (KDE). Based on this work, we then include a short discussion between the empirical model, designed in detail by an expert and the deep learning-based model that is learned from user data directly.

        @inproceedings{10.2312:mlvis.20191157
,
booktitle = {Machine Learning Methods in Visualisation for Big Data
},
editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
},
title = {{On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing
}},
author = {Fan, Chaoran and 
Hauser, Helwig
},
year = {2019
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-089-5
},
DOI = {10.2312/mlvis.20191157
}
}