Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data

arXiv:2307.03410v2 Announce Type: replace-cross
Abstract: Feature-distributed data, referred to data partitioned by features and stored across multiple computing nodes, are increasingly common in applications with a large number of features. This paper proposes a two-stage relaxed greedy algorithm (TSRGA) for applying multivariate linear regression to such data. The main advantage of TSRGA is that its communication complexity does not depend on the feature dimension, making it highly scalable to very large data sets. In addition, for multivariate response variables, TSRGA …

abstract algorithm applications arxiv computing cs.dc cs.lg data distributed distributed data feature features linear linear regression multiple multivariate nodes paper regression scalable stage stat.ml type