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Acceleration Approaches for Big Data Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
ISBN (Electronic)978-1-4799-7061-2
DOIs
Publication statusPublished - 6 Sep 2018
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 7 Oct 201810 Oct 2018

Publication series

Name
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Period7/10/1810/10/18

Abstract

The massive size of data that needs to be processed by Machine Learning models nowadays sets new challenges related to their computational complexity and memory footprint. These challenges span all processing steps involved in the application of the related models, i.e., from the fundamental processing steps needed to evaluate distances of vectors, to the optimization of large-scale systems, e.g. for non-linear regression using kernels, or the speed up of deep learning models formed by billions of parameters. In order to address these challenges, new approximate solutions have been recently proposed based on matrix/tensor decompositions, randomization and quantization strategies. This paper provides a comprehensive review of the related methodologies and discusses their connections.

Publication forum classification

Field of science, Statistics Finland