Multivariate and functional robust fusion methods for structured Big Data

Catherine Aaron, Alejandro Cholaquidis, Ricardo Fraiman, Badih Ghattas

Abstract: We address one of the important problems in Big Data, namely how to combine estimators from different subsamples by robust fusion procedures, when we are unable to deal with the whole sample. We propose a general framework based on the classic idea of ‘divide and conquer’. In particular we address in some detail the case of a multivariate location and scatter matrix, the covariance operator for functional data, and clustering problems.