TítuloRobust clustering of data collected via crowdsourcing
Tipo de publicaciónConference Paper
Year of Publication2017
AutoresPagès-Zamora, A, Giannakis, G, López-Valcarce, R, Gimenez-Febrer, P
Conference NameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Páginas4014 - 4018
Date Published03/2017
Conference LocationNew Orleans
Palabras clavewinter, wsn

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.