@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 = {<p><span style="color: rgb(51, 51, 51); font-family: sans-serif; font-size: 15px; background-color: rgb(255, 255, 255);">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.</span></p>},
	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}
}
