Manifold Gaussian Processes for Regression
File(s)
Accepted version
Author(s)
Calandra, R
Peters, J
Rasmussen, CE
Deisenroth, MP
Type
Conference Paper
Abstract
Off-the-shelf Gaussian Process (GP) covariance
functions encode smoothness assumptions on the structure
of the function to be modeled. To model complex and nondifferentiable
functions, these smoothness assumptions are often
too restrictive. One way to alleviate this limitation is to find
a different representation of the data by introducing a feature
space. This feature space is often learned in an unsupervised
way, which might lead to data representations that are not
useful for the overall regression task. In this paper, we propose
Manifold Gaussian Processes, a novel supervised method that
jointly learns a transformation of the data into a feature
space and a GP regression from the feature space to observed
space. The Manifold GP is a full GP and allows to learn data
representations, which are useful for the overall regression
task. As a proof-of-concept, we evaluate our approach on
complex non-smooth functions where standard GPs perform
poorly, such as step functions and robotics tasks with contacts.
functions encode smoothness assumptions on the structure
of the function to be modeled. To model complex and nondifferentiable
functions, these smoothness assumptions are often
too restrictive. One way to alleviate this limitation is to find
a different representation of the data by introducing a feature
space. This feature space is often learned in an unsupervised
way, which might lead to data representations that are not
useful for the overall regression task. In this paper, we propose
Manifold Gaussian Processes, a novel supervised method that
jointly learns a transformation of the data into a feature
space and a GP regression from the feature space to observed
space. The Manifold GP is a full GP and allows to learn data
representations, which are useful for the overall regression
task. As a proof-of-concept, we evaluate our approach on
complex non-smooth functions where standard GPs perform
poorly, such as step functions and robotics tasks with contacts.
Date Issued
2016-11-03
Date Acceptance
2016-03-15
Citation
2016 International Joint Conference on Neural Networks (IJCNN), 2016
ISSN
2161-4407
Publisher
IEEE
Journal / Book Title
2016 International Joint Conference on Neural Networks (IJCNN)
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
International Joint Conference on Neural Networks
Publication Status
Published
Start Date
2016-07-25
Finish Date
2016-07-29
Coverage Spatial
Vancouver, Canada