A randomized approach for crowdsourcing in the presence of multiple views

Yao Zhou; Jingrui He

doi:10.1109/ICDM.2017.78

A randomized approach for crowdsourcing in the presence of multiple views

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Driven by the dramatic growth of data both in terms of the size and sources, learning from heterogeneous data is emerging as an important research direction for many real applications. One of the biggest challenges of this type of problem is how to meaningfully integrate heterogeneous data to considerably improve the generality and quality of the learning model. In this paper, we first present a unified learning framework that aims to leverage the structural information from two types of data heterogeneity: view heterogeneity (as in multi-view learning) and worker heterogeneity (as in crowdsourcing). The objective follows the principles of view consistency and worker consensus by minimizing the loss term with a regularized prediction tensor. We then propose to relax and solve the optimization framework with an iterative updating method. We also prove that the gradient of the most time-consuming updating block is separable with respect to the workers, which leads to a randomized algorithm with faster speed and better convergence. Finally, we compare the proposed method with several state-of-the-arts and demonstrate its effectiveness on various data sets.

Original language	English (US)
Title of host publication	Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
Editors	George Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	685-694
Number of pages	10
ISBN (Electronic)	9781538638347
DOIs	https://doi.org/10.1109/ICDM.2017.78
State	Published - Dec 15 2017
Externally published	Yes
Event	17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States Duration: Nov 18 2017 → Nov 21 2017

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
Volume	2017-November
ISSN (Print)	1550-4786

Other

Other	17th IEEE International Conference on Data Mining, ICDM 2017
Country/Territory	United States
City	New Orleans
Period	11/18/17 → 11/21/17

Keywords

Crowdsourcing
Heterogeneous Learning
Multi-view Learning
Optimization

ASJC Scopus subject areas

Engineering(all)

Online availability

10.1109/ICDM.2017.78

Library availability

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Cite this

Zhou, Y., & He, J. (2017). A randomized approach for crowdsourcing in the presence of multiple views. In G. Karypis, S. Alu, V. Raghavan, X. Wu, & L. Miele (Eds.), Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017 (pp. 685-694). (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2017.78

A randomized approach for crowdsourcing in the presence of multiple views. / Zhou, Yao; He, Jingrui.
Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. ed. / George Karypis; Srinivas Alu; Vijay Raghavan; Xindong Wu; Lucio Miele. Institute of Electrical and Electronics Engineers Inc., 2017. p. 685-694 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2017-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhou, Y & He, J 2017, A randomized approach for crowdsourcing in the presence of multiple views. in G Karypis, S Alu, V Raghavan, X Wu & L Miele (eds), Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2017-November, Institute of Electrical and Electronics Engineers Inc., pp. 685-694, 17th IEEE International Conference on Data Mining, ICDM 2017, New Orleans, United States, 11/18/17. https://doi.org/10.1109/ICDM.2017.78

Zhou Y, He J. A randomized approach for crowdsourcing in the presence of multiple views. In Karypis G, Alu S, Raghavan V, Wu X, Miele L, editors, Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 685-694. (Proceedings - IEEE International Conference on Data Mining, ICDM). doi: 10.1109/ICDM.2017.78

Zhou, Yao ; He, Jingrui. / A randomized approach for crowdsourcing in the presence of multiple views. Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. editor / George Karypis ; Srinivas Alu ; Vijay Raghavan ; Xindong Wu ; Lucio Miele. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 685-694 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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A randomized approach for crowdsourcing in the presence of multiple views

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Keywords

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