@conference {876, title = {Robust clustering of data collected via crowdsourcing}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2017}, month = {03/2017}, pages = {4014 - 4018}, address = {New Orleans}, 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 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.

}, keywords = {winter, wsn}, doi = {10.1109/ICASSP.2017.7952910}, author = {Alba Pag{\`e}s-Zamora and Georgios Giannakis and R. L{\'o}pez-Valcarce and Pere Gimenez-Febrer} }