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Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes

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Title: Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes
Authors: Soh, H
Demiris, Y
Item Type: Journal Article
Abstract: Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair.
Issue Date: 1-Mar-2015
Date of Acceptance: 28-Mar-2014
URI: http://hdl.handle.net/10044/1/18527
DOI: 10.1109/TNNLS.2014.2316291
ISSN: 2162-2388
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 522
End Page: 536
Journal / Book Title: IEEE Transactions on Neural Networks and Learning Systems
Volume: 26
Issue: 3
Copyright Statement: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 248116
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Gaussian processes (GPs)
machine learning
recurrent neural networks (RNNs)
time-series analysis
Humans
Machine Learning
Neural Networks, Computer
Normal Distribution
Pattern Recognition, Automated
Robotics
Time Factors
Humans
Normal Distribution
Robotics
Time Factors
Pattern Recognition, Automated
Machine Learning
Neural Networks, Computer
Artificial Intelligence & Image Processing
Publication Status: Published
Online Publication Date: 2014-06-12
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering