@conference {960, title = {Low-rank data matrix recovery with missing values and faulty sensors}, booktitle = {European Signal Processing Conference (EUSIPCO)}, year = {2019}, month = {09/2019}, address = {A Coru{\~n}a, Spain}, abstract = {

In practice, data gathered by wireless sensor networks often belongs in a low-dimensional subspace, but it can present missing as well as corrupted values due to sensor malfunctioning and/or malicious attacks. We study the problem of Maximum Likelihood estimation of the low-rank factors of the underlying structure in such situation, and develop an Expectation-Maximization algorithm to this purpose, together with an effective initialization scheme. The proposed method outperforms previous schemes based on an initial faulty sensor identification stage, and is competitive in terms of complexity and performance with convex optimization-based matrix completion approaches.

}, keywords = {winter, wsn}, author = {R. L{\'o}pez-Valcarce and Josep Sala} } @conference {845, title = {Distributed multivariate regression with unknown noise covariance in the presence of outliers: a minimum description length approach}, booktitle = {IEEE Workshop on Statistical Signal Processing (SSP)}, year = {2016}, address = {Palma de Mallorca, Spain}, abstract = {
We consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network
which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed
unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood
estimate is investigated, with the number of outliers being estimated according to the Minimum Description Length
principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective
for managing outliers in the data.
}, keywords = {compass, wsn}, author = {R. L{\'o}pez-Valcarce and Daniel Romero and Josep Sala and Alba Pag{\`e}s-Zamora} }