Título | Robust clustering of data collected via crowdsourcing |
Tipo de publicación | Conference Paper |
Year of Publication | 2017 |
Autores | Pagès-Zamora, A, Giannakis, G, López-Valcarce, R, Gimenez-Febrer, P |
Conference Name | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
Páginas | 4014 - 4018 |
Date Published | 03/2017 |
Conference Location | New Orleans |
Palabras clave | winter, wsn |
Resumen | 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. |
DOI | 10.1109/ICASSP.2017.7952910 |