Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published September 2019 | public
Journal Article

Multitask Sparse Bayesian Learning with Applications in Structural Health Monitoring

Abstract

We focus on a Bayesian approach to learn sparse models by simultaneously utilizing multiple groups of measurements that are marked by a similar sparseness profile. Joint learning of sparse representations for multiple models has been mostly overlooked, although it is a useful tool for exploiting data redundancy by modeling informative relationships within groups of measurements. To this end, two hierarchical Bayesian models are introduced and associated algorithms are proposed for multitask sparse Bayesian learning (SBL). It is shown that the data correlations for different tasks are taken into account more effectively by using the hierarchical model with a common prediction‐error precision parameter across all related tasks, which leads to a better learning performance. Numerical experiments verify that exploiting common information among multiple related tasks leads to better performance, for both models that are highly and approximately sparse. Then, we examine two applications of multitask SBL in structural health monitoring: identifying structural stiffness losses and recovering missing data occurring during wireless transmission, which exploit information about relationships in the temporal and spatial domains, respectively. These illustrative examples demonstrate the potential of multitask SBL for solving a wide range of sparse approximation problems in science and technology.

Additional Information

© 2018 Computer‐Aided Civil and Infrastructure Engineering. Issue Online: 04 August 2019; Version of Record online: 21 August 2018. Funding Information: National Natural Science Foundation of China. Grant Numbers: 51778192, 51638007. National Key Research and Development Program of China. Grant Number: 2017YFC1500605

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023