Robust clustering of data collected via crowdsourcing
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10.1109/ICASSP.2017.7952910
Inclou dades d'ús des de 2022
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hdl:2117/108122
Tipus de documentText en actes de congrés
Data publicació2017
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Abstract
Crowdsourcing approaches rely on the collection of multiple individuals to solve problems that require analysis of large data sets in a timely accurate manner. The inexperience of participants or annotators motivates well robust techniques. Focusing on clustering setups, the data provided by all an- notators is suitably modeled here as a mixture of Gaussian components plus a uniformly distributed random variable to capture outliers. The proposed algorithm is based on the expectation-maximization algorithm and allows for soft as- signments of data to clusters, to rate annotators according to their performance, and to estimate the number of Gaussian components in the non-Gaussian/Gaussian mixture model, in a jointly manner.
Descripció
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CitacióPages, A., Giannakis, G.B., López, R., Gimenez, P. Robust clustering of data collected via crowdsourcing. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "ICASSP 2017 - 42nd IEEE International Conference on Acoustics, Speech and Signal Processing: March 5-9, 2017:New Orleans, USA: Proceedings book". New Orleans: 2017, p. 4014-4018.
Versió de l'editorhttp://ieeexplore.ieee.org/document/7952910/
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