کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
411828 679592 2012 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Data fusion with Gaussian processes
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Data fusion with Gaussian processes
چکیده انگلیسی

This paper addresses the problem of fusing multiple sets of heterogeneous sensor data using Gaussian processes (GPs). Experiments on large scale terrain modeling in mining automation are presented. Three techniques in increasing order of model complexity are discussed. The first is based on adding data to an existing GP model. The second approach treats data from different sources as different noisy samples of a common underlying terrain and fusion is performed using heteroscedastic GPs. The final approach, based on dependent GPs, models each data set by a separate GP and learns spatial correlations between data sets through auto and cross covariances. The paper presents a unifying view of approaches to data fusion using GPs, a statistical evaluation that compares these approaches and multiple previously untested variants of them and an insight into the effect of model complexity on data fusion. Experiments suggest that in situations where data being fused is not rich enough to require a complex GP data fusion model or when computational resources are limited, the use of simpler GP data fusion techniques, which are constrained versions of the more generic models, reduces optimization complexity and consequently can enable superior learning of hyperparameters, resulting in a performance gain.


► Data fusion with Gaussian processes (GPs), Heteroscedastic GPs and Dependent GPs.
► Unifying view of approaches to data fusion with Gaussian processes (GPDF).
► Benchmarking and complexity analysis of GPDF methods, variants and a naive approach.
► Depending on data complexity, simpler GPDF methods may outperform more complex ones.
► Experiments on large scale real sensor data; statistically representative evaluation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Robotics and Autonomous Systems - Volume 60, Issue 12, December 2012, Pages 1528–1544
نویسندگان
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