ML Anthology

A Trust-Based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing

Qikun Xiang, Jie Zhang, Ido Nevat, Pengfei Zhang
IJCAI 2017 pp. 3866-3872
doi:10.24963/IJCAI.2017/540 /ijcai/2017/xiang2017ijcai-trust/

Abstract

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result in biased and inaccurate estimation and decision making. We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. Experiments using two real-world datasets show the superior robustness of our model compared with existing approaches.

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Text

Xiang et al. "A Trust-Based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/540

Markdown

[Xiang et al. "A Trust-Based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/xiang2017ijcai-trust/) doi:10.24963/IJCAI.2017/540

BibTeX

@inproceedings{xiang2017ijcai-trust,
  title     = {{A Trust-Based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing}},
  author    = {Xiang, Qikun and Zhang, Jie and Nevat, Ido and Zhang, Pengfei},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {3866-3872},
  doi       = {10.24963/IJCAI.2017/540},
  url       = {https://mlanthology.org/ijcai/2017/xiang2017ijcai-trust/}
}