Robust Fusion Methods for Structured Big Data

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 addr...

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Bibliographic Details
Published in:arXiv.org 2018-04
Main Authors: Aaron, Catherine, Cholaquidis, Alejandro, Fraiman, Ricardo, Ghattas, Badih
Format: Article
Language:English
Subjects:
Big Data
Clustering
Covariance
Data management
Online Access:Get full text
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Summary: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.
ISSN:2331-8422