Deep Learning Inverse Multidimensional Projections

Espadoto, Mateus; Rodrigues, Francisco Caio Maia; Hirata, Nina S. T.; Hirata Jr., Roberto; Telea, Alexandru C.

Deep Learning Inverse Multidimensional Projections

Files

013-017.pdf (4.71 MB)

Date

2019

Authors

Espadoto, Mateus
Rodrigues, Francisco Caio Maia
Hirata, Nina S. T.
Hirata Jr., Roberto
Telea, Alexandru C.

Publisher

The Eurographics Association

Abstract

We present a new method for computing inverse projections from 2D spaces to arbitrary high-dimensional spaces. Given any projection technique, we train a deep neural network to learn a low-to-high dimensional mapping based on a projected training set, and next use this mapping to infer the mapping on arbitrary points. We compare our method with two recent inverse projection techniques on three datasets, and show that our method has similar or higher accuracy, is one to two orders of magnitude faster, and delivers result that match well known ground-truth information about the respective high-dimensional data. Visual analytics Unsupervised learning Dimensionality reduction and manifold learning.

        @inproceedings{10.2312:eurova.20191118
,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)
},
editor = {Landesberger, Tatiana von and Turkay, Cagatay
},
title = {{Deep Learning Inverse Multidimensional Projections
}},
author = {Espadoto, Mateus and 
Rodrigues, Francisco Caio Maia and 
Hirata, Nina S. T. and 
Hirata Jr., Roberto and 
Telea, Alexandru C.
},
year = {2019
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-087-1
},
DOI = {10.2312/eurova.20191118
}
}