Classifying Continuous Labels: A Simple Tweak to Make Regression Robust

Master thesis (2022)

Authors

Z. Bao Electrical Engineering, Mathematics and Computer Science

Contributors

J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
S. Pintea Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
C.C.S. Liem (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Ziyu Bao
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Published Date

04-07-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Regression is difficult because of noise, imbalanced data sampling, missing data, etc. We propose a method by classifying the continuous regression labels to tackle regression robustness problems. We analyze if our method can help regression, given that the class information is already included in the regression labels. We start by extensively experimenting on 1D synthetic datasets and find out that classification can help regression when the data sampling is i balanced. This happens when the data are clean, noisy in inputs and noisy in outputs, but not when they are partially missing. We then validate our conclusion on the KITTI dataset by estimating 3D object orientation. We conclude that our method can help regression in real-world.