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Hierarchical Data Reduction and Learning by Shekhar, Prashant; Patra, Abani is a scholarly article available to read on EtoBox.

What is Hierarchical Data Reduction and Learning about?

This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at successively finer scales. A detailed analysis of stability, convergence and behavior of error functionals associated with the approximations are presented, along with a well chosen set of applications. Results show the performance of the approach as a data reduction mechanism for both synthetic (univariate and multivariate) and real datasets (geospatial and numerical model outcomes). The sparse representation generated is shown to efficiently reconstruct data and minimize error in prediction.

Author

Shekhar, Prashant; Patra, Abani

Published

2019

Language

EN