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.

%B IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) %C New Orleans %P 4014 - 4018 %8 03/2017 %G eng %R 10.1109/ICASSP.2017.7952910 %0 Conference Paper %B European Signal Processing Conference (EUSIPCO) %D 2016 %T Online EM-based distributed estimation in sensor networks with faulty nodes %A Pere Gimenez-Febrer %A Alba Pagès-Zamora %A R. López-Valcarce %K compass %K wsn %XThis paper focuses on the problem of the distributed estimation of a parameter vector based on noisy observations regularly acquired by the nodes of a wireless sensor network and assuming that some of the nodes have faulty sensors. We propose two online schemes, both centralized and distributed, based on the Expectation-Maximization (EM) algorithm. These algorithms are able to identify and disregard the faulty nodes, and provide a refined estimate of the parameters each time instant after a new set of observations is acquired. Simulation results demonstrate that the centralized versions of the proposed online algorithms attain the same estimation error as the centralized batch EM, whereas the distributed versions come very close to matching the batch EM.

%B European Signal Processing Conference (EUSIPCO)
%C Budapest, Hungary
%8 09/2016
%G eng
%0 Conference Paper
%B IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
%D 2015
%T Distributed AoA-based Source Positioning in NLOS with Sensor Networks
%A Pere Gimenez-Febrer
%A Alba Pagès-Zamora
%A Silvana Silva Pereira
%A R. López-Valcarce
%K compass
%K wsn
%X This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non-line-of-sight (NLOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.

%B IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
%I IEEE
%C Brisbane, Australia
%8 04/2015
%G eng