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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 4
1119-1129

Multi-label crowd answer aggregation of digital cultural heritage based on adaptive graph regularization and joint low-rank matrix factorization

Wang Chunxue1,2
Xu Linlin3
Yu Tianxiu1,2
1. Gansu Provincial Research Center for Conservation of Dunhuang Cultural Heritage, Dunhuang Gansu 736200, China
2. Cultural Heritage Digitization Institute, Dunhuang Academy, Dunhuang Gansu 736200, China
3. School of Computer Information & Management, Inner Mongolia University of Finance & Economics, Hohhot 010070, China
DOI: 10.19734/j.issn.1001-3695.2022.09.0442

Abstract

Multi-label answer aggregation problem aims to estimate the ground truth labels of samples by aggregating a large number of non-expert annotations collected by crowdsourcing. Due to the high annotation cost, multiple sample categories and uneven distribution of digital cultural heritage data, it brings great challenges to multi-label answer aggregation of datasets. Previous methods mainly focus on single-label problems, ignoring the label relevance of multi-label tasks. To some extent, most multi-label aggregation methods consider label correlations but are sensitive to noises and outliers. To solve these problems, this paper proposed a multi-label answer aggregation method based on adaptive graph regularization and joint low-rank matrix factorization AGR-JMF. Firstly, it divided the input annotation matrix into two parts: pure annotations and noise annotations. Then, it constructed the association matrix between labels by adaptive graph regularization method for pure annotations. Finally, in order to realize the multi-label answer aggregations, it used labeling quality, label relevance, and the behavior attributes similarity between annotators to guide the low-rank matrix factorization. Experiments on real-world datasets and MGF dataset show that AGR-JMF has obvious advantages over existing algorithms in terms of aggregating accuracy and identifying unreliable annotators.

Foundation Support

甘肃省敦煌文物保护研究中心开放课题(GDW2021YB05)
陇原青年创新创业人才(个人)资助项目(2022LQGR40)
国家重点研发计划资助项目(2020YFC1522701,2020YFC1522705)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.09.0442
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 4
Section: Algorithm Research & Explore
Pages: 1119-1129
Serial Number: 1001-3695(2023)04-026-1119-11

Publish History

[2022-12-07] Accepted Paper
[2023-04-05] Printed Article

Cite This Article

王春雪, 徐琳琳, 俞天秀. 基于自适应图正则化与联合低秩矩阵分解的数字文化遗产多标签众包答案聚合方法 [J]. 计算机应用研究, 2023, 40 (4): 1119-1129. (Wang Chunxue, Xu Linlin, Yu Tianxiu. Multi-label crowd answer aggregation of digital cultural heritage based on adaptive graph regularization and joint low-rank matrix factorization [J]. Application Research of Computers, 2023, 40 (4): 1119-1129. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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