DocumentCode :
178638
Title :
Circular Regression Based on Gaussian Processes
Author :
Guerrero, P. ; Ruiz del Solar, J.
Author_Institution :
Dept. de Cienc. de la Comput., Univ. de Chile, Santiago, Chile
fYear :
2014
fDate :
24-28 Aug. 2014
Firstpage :
3672
Lastpage :
3677
Abstract :
Circular data is very relevant in many fields such as Geostatistics, Mobile Robotics and Pose Estimation. However, some existing angular regression methods do not cope with arbitrary nonlinear functions properly. Moreover, some other regression methods that do cope with nonlinear functions, like Gaussian Processes, are not designed to work well with angular responses. This paper presents two novel methods for circular regression based on Gaussian Processes. The proposed methods were tested on both synthetic data from basic functions, and real data obtained from a computer vision application. In these experiments, both proposed methods showed superior performance to that of Gaussian Processes.
Keywords :
Gaussian processes; regression analysis; Gaussian processes; angular regression methods; angular responses; arbitrary nonlinear functions; basic functions; circular regression; computer vision application; geostatistics; mobile robotics; pose estimation; real data; synthetic data; Covariance matrices; Estimation; Feature extraction; Gaussian processes; Noise; Training; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2014 22nd International Conference on
Conference_Location :
Stockholm
ISSN :
1051-4651
Type :
conf
DOI :
10.1109/ICPR.2014.631
Filename :
6977343
Link To Document :
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