Robust Bayesian Transfer Learning Between Kalman Filters

Papež, Milan; Quinn, Anthony

doi:10.1109/mlsp.2019.8918783

“…Transfer learning (TL) differs from other machine learning methods in that the data involved can originate from different tasks or have different domains. It aims to improve the performance of a primary source task by utilizing information learned from multiple learning sources that may perform the same or similar tasks but under different conditions (Arnold et al, 2007;Pan and Yang, 2010;Torrey and Shavlik, 2010;Weiss et al, 2016;Karbalayghareh et al, 2018;Kouw and Loog, 2019;Papež and Quinn, 2019). This is specifically important when sufficient data is not available at the primary source or when labeling the data is problematic.…”

Section: Transfer Learningmentioning

confidence: 99%

“…Different machine learning methods have been used, for example, to overcome limitations due to various assumptions on the sensing environment and to solve complex inference problems. Transfer learning is a machine learning method used to transfer and apply knowledge that is learned from previous tasks to solve a current task (Pan and Yang, 2010;Torrey and Shavlik, 2010;Karbalayghareh et al, 2018;Kouw and Loog, 2019;Papež and Quinn, 2019). This method is particularly advantageous when the data provided for inference is not sufficient or is difficult to label (Jaini et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Nonparametric Learning and Knowledge Transfer for Object Tracking Under Unknown Time-Varying Conditions

Alotaibi

¹

,

Papandreou-Suppappola

²

2022

Front. Signal Process.

0

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We consider the problem of a primary source tracking a moving object under time-varying and unknown noise conditions. We propose two methods that integrate sequential Bayesian filtering with transfer learning to improve tracking performance. Within the transfer learning framework, multiple sources are assumed to perform the same tracking task as the primary source but under different noise conditions. The first method uses Gaussian mixtures to model the measurement distribution, assuming that the measurement noise intensity at the learning sources is fixed and known a priori and the learning and primary sources are simultaneously tracking the same source. The second tracking method uses Dirichlet process mixtures to model noise parameters, assuming that the learning source measurement noise intensity is unknown. As we demonstrate, the use of Bayesian nonparametric learning does not require all sources to track the same object. The learned information can be stored and transferred to the primary source when needed. Using simulations for both high- and low-signal-to-noise ratio conditions, we demonstrate the improved primary tracking performance as the number of learning sources increases.

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“…Transfer learning (TL) differs from other machine learning methods in that the data involved can originate from different tasks or have different domains. It aims to improve the performance of a primary source task by utilizing information learned from multiple learning sources that may perform the same or similar tasks but under different conditions (Arnold et al, 2007;Pan and Yang, 2010;Torrey and Shavlik, 2010;Weiss et al, 2016;Karbalayghareh et al, 2018;Kouw and Loog, 2019;Papež and Quinn, 2019). This is specifically important when sufficient data is not available at the primary source or when labeling the data is problematic.…”

Section: Transfer Learningmentioning

confidence: 99%

“…Different machine learning methods have been used, for example, to overcome limitations due to various assumptions on the sensing environment and to solve complex inference problems. Transfer learning is a machine learning method used to transfer and apply knowledge that is learned from previous tasks to solve a current task (Pan and Yang, 2010;Torrey and Shavlik, 2010;Karbalayghareh et al, 2018;Kouw and Loog, 2019;Papež and Quinn, 2019). This method is particularly advantageous when the data provided for inference is not sufficient or is difficult to label (Jaini et al, 2017).…”

Section: Introductionmentioning

confidence: 99%