%0 Conference Paper %B IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) %D 2017 %T Robust clustering of data collected via crowdsourcing %A Alba Pagès-Zamora %A Georgios Giannakis %A R. López-Valcarce %A Pere Gimenez-Febrer %K winter %K wsn %X

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 annotators 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 assignments 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.

%B IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) %C New Orleans %P 4014 - 4018 %8 03/2017 %G eng %R 10.1109/ICASSP.2017.7952910