@inproceedings{bc373bb58aab47c4833ea6f89a90b3ef,
title = "Approximating global optimum for probabilistic truth discovery",
abstract = "The problem of truth discovery arises in many areas such as database, data mining, data crowdsourcing and machine learning. It seeks trustworthy information from possibly conflicting data provided by multiple sources. Due to its practical importance, the problem has been studied extensively in recent years. Two competing models were proposed for truth discovery, weight-based model and probabilistic model. While (Formula Presented) -approximations have already been obtained for the weight-based model, no quality guaranteed solution has been discovered yet for the probabilistic model. In this paper, we focus on the probabilistic model and formulate it as a geometric optimization problem. Based on a sampling technique and a few other ideas, we achieve the first (Formula Presented) -approximation solution. The general technique we developed has the potential to be used to solve other geometric optimization problems.",
keywords = "Data mining, Geometric optimization, High-dimension, Truth discovery",
author = "Shi Li and Jinhui Xu and Minwei Ye",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 24th International Conference on Computing and Combinatorics Conference, COCOON 2018 ; Conference date: 02-07-2018 Through 04-07-2018",
year = "2018",
doi = "10.1007/978-3-319-94776-1\_9",
language = "English",
isbn = "9783319947754",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "96--107",
editor = "Daming Zhu and Lusheng Wang",
booktitle = "Computing and Combinatorics - 24th International Conference, COCOON 2018, Proceedings",
address = "Germany",
}