TY - CONF T1 - Robust clustering of data collected via crowdsourcing T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Y1 - 2017 A1 - Alba Pagès-Zamora A1 - Georgios Giannakis A1 - R. López-Valcarce A1 - Pere Gimenez-Febrer KW - winter KW - wsn AB -

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.

JF - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) CY - New Orleans ER -