Abstract
Crowdsourcing is a useful and economic approach to annotate data. Various computational solutions have been developed to pursue a consensus of high quality. However, available solutions mainly target single-label tasks, and they neglect correlations among labels. In this paper, we introduce a multi-label crowd consensus (MLCC) model based on a joint matrix factorization. Specifically, MLCC selectively and jointly factorizes the sample-label association matrices into products of individual and shared low-rank matrices. As such, it makes use of the robustness of low-rank matrix approximation to noisy annotations and diminishes the impact of unreliable annotators by assigning small weights to their annotation matrices. To obtain coherent low-rank matrices, MLCC additionally leverages the shared low-rank matrix to model correlations among labels, and the individual low-rank matrices to measure the similarity between annotators. MLCC then computes the low-rank matrices and weights via a unified objective function, and adopts an alternative optimization technique to iteratively optimize them. Finally, MLCC uses the optimized low-rank matrices and weights to compute the consensus labels. Our experimental results demonstrate that MLCC outperforms competitive methods in inferring consensus labels. Besides identifying spammers, MLCC achieves robustness against their incorrect annotations, by crediting them small, or zero, weights.
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References
Abbas Q, Celebi ME, Serrano C, GarcíA IF, Ma G (2013) Pattern classification of dermoscopy images: a perceptually uniform model. Pattern Recognit. 46(1):86–97
Bragg J, Weld DS (2013) Crowdsourcing multi-label classification for taxonomy creation. In: 1st AAAI conference on human computation and crowdsourcing
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge
Chen X, Yu G, Domeniconi C, Wang J, Li Z, Zhang Z (2018) Cost effective multi-label active learning via querying subexamples. In: IEEE international conference on data mining, pp 905–910
Chen X, Yu G, Domeniconi C, Wang J, Zhang Z (2018) Matrix factorization for identifying noisy labels of multi-label instances. In: Pacific Rim international conference on artificial intelligence, pp 508–517
Difallah DE, Demartini G, Cudré-Mauroux P (2012) Mechanical cheat: spamming schemes and adversarial techniques on crowdsourcing platforms. In: Proceedings of the first international workshop on crowdsourcing web search, Lyon, France, pp 26–30
Dawid AP, Skene AM (1979) Maximum likelihood estimation of observer error-rates using the EM algorithm. Appl Stat 28(1):20–28
Duan L, Oyama S, Sato H, Kurihara M (2014) Separate or joint? Estimation of multiple labels from crowdsourced annotations. Expert Syst Appl 41(13):5723–5732
Duan L, Oyama S, Kurihara M, Sato H (2015) Crowdsourced semantic matching of multi-label annotations. In: Proceedings of international joint conference on artificial intelligence, pp 3483–3489
Demartini G, Difallah DE, Cudr-Mauroux P (2012) ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In: Proceedings of the 21st international conference on world wide web, pp 469–478
Elisseeff A, Weston J (2001) A kernel method for multi-labelled classification. In: Advances in neural information processing systems (NeurIPS), Vancouver, British Columbia, Canada, 3–8 Dec 2001, pp 681–687
Ekman P (1992) An argument for basic emotions. Cognit Emot 6(3–4):169–200
Franklin MJ, Kossmann D, Kraska T, Ramesh S, Xin R (2011) CrowdDB: answering queries with crowdsourcing. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data, pp 61–72
Gokhale C, Das S, Doan A, Naughton JF, Rampalli N, Shavlik J, Zhu X (2014) Corleone: hands-off crowdsourcing for entity matching. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data, pp 601–612
Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surv (CSUR) 47(3):52
Godbole S, Sarawagi S (2004) Discriminative methods for multi-labeled classification. In: Proceedings of Pacific-Asia conference on knowledge discovery and data mining, pp 22–30
Ho CJ, Vaughan JW (2012) Online task assignment in crowdsourcing markets. Proc AAAI Conf Artif Intell 12:45–51
Hung NQV, Viet HH, Tam NT, Weidlich M, Yin H, Zhou X (2018) Computing crowd consensus with partial agreement. IEEE Trans Know Data Eng 30(1):1–14
Hung NQV, Nguyen TT, Lam NT, Aberer K (2013) An evaluation of aggregation techniques in crowdsourcing. In: International conference on web information systems engineering, Nanjing, China, 13–15 Oct 2013, pp 1–15
Howe J (2006) The rise of crowdsourcing. Wired Mag 14(6):1–4
Kazai G, Kamps J, Koolen M, Milic-Frayling N (2011) Crowdsourcing for book search evaluation: impact of hit design on comparative system ranking. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 205–214
Kazai G, Kamps J, Milic-Frayling N (2011). Worker types and personality traits in crowdsourcing relevance labels. In: Proceedings of the 20th ACM international conference on information and knowledge management, pp 1941–1944
Kazai G, Kamps J, Milic-Frayling N (2012) The face of quality in crowdsourcing relevance labels: demographics, personality and labeling accuracy. In: Proceedings of the 21st ACM international conference on information and knowledge management, pp 2583–2586
Kurve A, Miller DJ, Kesidis G (2015) Multicategory crowdsourcing accounting for variable task difficulty, worker skill, and worker intention. IEEE Trans Knowl Data Eng 27(3):794–809
Kamar E, Kapoor A, Horvitz E (2015) Identifying and accounting for task-dependent bias in crowdsourcing. In: 3rd AAAI conference on human computation and crowdsourcing
Karger DR, Oh S, Shah D (2011). Budget-optimal crowdsourcing using low-rank matrix approximations. In: 49th Annual Allerton conference on communication, control, and computing, pp 284–291
Konstantinides K, Natarajan B, Yovanof GS (1997) Noise estimation and filtering using block-based singular value decomposition. IEEE Trans Image Process 6(3):479–483
Kovashka A, Russakovsky O, Fei-Fei L, Grauman K (2016) Crowdsourcing in computer vision. Foundations and trends® in computer graphics and vision 10(3):177–243
Nakamura A (1993) Kanjo Hyogen Jiten (Dictionary of emotive expressions). Tokyodo Publishing, Tokyo
Lease M, Yilmaz E (2012) Crowdsourcing for information retrieval. ACM SIGIR Forum 45(2):66–75
Li SY, Jiang Y, Zhou ZH (2015) Multi-label active learning from crowds. arXiv preprint arXiv:1508.00722
Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788
Meng D, De La Torre F (2013) Robust matrix factorization with unknown noise. In: Proceedings of the IEEE international conference on computer vision, pp 1337–1344
Moreno PG, Artés-Rodríguez A, Teh YW, Perez-Cruz F (2015) Bayesian nonparametric crowdsourcing. J Mach Learn Res 16(1):1607–1627
Meng R, Tong Y, Chen L, Cao CC (2015) CrowdTC: crowdsourced taxonomy construction. In: IEEE international conference on data mining, pp 913–918
Nie F, Wang H, Cai X, Huang H, Ding C (2012) Joint Schatten \(p\)-norm and \(l_p\)-norm robust matrix completion for missing value recovery. Knowl Inf Syst 42(3):525–544
Nowak S, Rüger S (2010) How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. In: Proceedings of the international conference on multimedia information retrieval, pp 557–566
Otani N, Baba Y, Kashima H (2015) Quality control for crowdsourced hierarchical classification. In: IEEE international conference on data mining, pp 937–942
Rahman H, Roy SB, Thirumuruganathan S, Amer-Yahia S, Das G (2015) Task assignment optimization in collaborative crowdsourcing. In: IEEE international conference on data mining, pp 949–954
Raykar VC, Yu S (2012) Eliminating spammers and ranking annotators for crowdsourced labeling tasks. J Mach Learn Res 13(2):491–518
Raykar VC, Yu S, Zhao LH, Valadez GH, Florin C, Bogoni L, Moy L (2010) Learning from crowds. J Mach Learn Res 11:1297–1322
Smilde AK, van der Werf MJ, Bijlsma S, van der Werff-van der Vat BJ, Jellema RH (2005) Fusion of mass spectrometry-based metabolomics data. Anal Chem 77(20):6729–6736
Sheng VS, Provost F, Ipeirotis PG (2008) Get another label? improving data quality and data mining using multiple, noisy labelers. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 614–622
Smilde AK, Kiers HA, Bijlsma S, Rubingh CM, Van Erk MJ (2008) Matrix correlations for high-dimensional data: the modified RV-coefficient. Bioinformatics 25(3):401–405
Tu J, Yu G, Domeniconi C, Wang J, Xiao G, Guo M (2018) Multi-label answer aggregation based on joint matrix factorization. In: IEEE international conference on data mining pp 517–526
Tsoumakas G, Katakis I, Vlahavas I (2011) Random k-labelsets for multilabel classification. IEEE Trans Know Data Eng 23(7):1079–1089
Vuurens J, de Vries AP, Eickhoff C (2011) How much spam can you take? An analysis of crowdsourcing results to increase accuracy. In: Proceeding ACM SIGIR workshop on crowdsourcing for information retrieval, pp 21–26
Venanzi M, Guiver J, Kohli P, Jennings NR (2016) Time-sensitive Bayesian information aggregation for crowdsourcing systems. J Artif Intell Res 56:517–545
Venanzi M, Guiver J, Kazai G, Kohli P, Shokouhi M (2014) Community-based bayesian aggregation models for crowdsourcing. In: Proceedings of the 23rd international conference on world wide web, pp 155–164
Wang A, Hoang CDV, Kan MY (2013) Perspectives on crowdsourcing annotations for natural language processing. Lang Resour Eval 47(1):9–31
Wang W, Guo XY, Li SY, Jiang Y, Zhou ZH (2017) Obtaining high-quality label by distinguishing between easy and hard items in crowdsourcing. In: International joint conference on artificial intelligence, pp 2964–2970
Whitehill J, Ruvolo P, Wu T, Bergsma J, Movellan JR (2009) Whose vote should count more: optimal integration of labels from labelers of unknown expertise. In: Advances in neural information processing systems, Vancouver, British Columbia, Canada, 7–10 Dec 2009, pp 2035–2043
Wu M, Wu X (2019) On big wisdom. Knowl Inf Syst 58(1):1–8
Xu L, Wang Z, Shen Z, Wang Y, Chen E (2014) Learning low-rank label correlations for multi-label classification with missing labels. In: IEEE international conference on data mining, pp 1067–1072
Yoshimura K, Baba Y, Kashima H (2017) Quality control for crowdsourced multi-label classification using RAkEL. In: International conference on neural information processing, pp 64–73
Yu G, Zhang G, Zhang Z, Yu Z, Deng L (2015) Semi-supervised classification based on subspace sparse representation. Knowl Inf Syst 43(1):81–101
Yu G, Chen X, Domeniconi C, Wang J, Li Z, Zhang Z, Wu X (2018) Feature-induced partial multi-label learning. In: IEEE international conference on data mining pp 1398–1403
Zhang J, Wu X, Sheng VS (2016) Learning from crowdsourced labeled data: a survey. Artif Intell Rev 46(4):543–576
Zhang J, Wu X, Sheng VS (2015) Imbalanced multiple noisy labeling. IEEE Trans Knowl Data Eng 27(2):489–503
Zhang J, Wu X (2018) Multi-Label Inference for Crowdsourcing. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 2738–2747
Zhang J, Sheng VS, Li Q, Wu J, Wu X (2017) Consensus algorithms for biased labeling in crowdsourcing. Inf Sci 382:254–273
Zhang ML, Zhang K (2010) Multi-label learning by exploiting label dependency. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 999–1008
Zhang ML, Zhou ZH (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
Zhang Y, Chen X, Zhou D, Jordan MI (2014) Spectral methods meet EM: a provably optimal algorithm for crowdsourcing. In: Advances in neural information processing systems, Montreal, Quebec, Canada, 8–13 Dec 2014, pp 1260–1268
Zhou ZH, Li M (2010) Semi-supervised learning by disagreement. Knowl Inf Syst 24(3):415–439
Acknowledgements
We thank the authors who kindly shared their source code and datasets with us for the experiments, the anonymous reviewers for their comments on improving this paper, and Mr. Jia Bin for maintaining the computing resources. This research is supported by NSFC (61872300, 61741217,61873214 and 61871020), Fundamental Research Funds for the Central Universities (XDJK2019B024), the Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228,cstc2016jcyjA035).
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Tu, J., Yu, G., Domeniconi, C. et al. Multi-label crowd consensus via joint matrix factorization. Knowl Inf Syst 62, 1341–1369 (2020). https://doi.org/10.1007/s10115-019-01386-7
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DOI: https://doi.org/10.1007/s10115-019-01386-7