Acceleration Approaches for Big Data Analysis
Author | Muravev A. |
Author | Tran D.T. |
Author | Iosifidis A. |
Author | Kiranyaz S. |
Author | Gabbouj M. |
Available date | 2020-03-03T06:19:36Z |
Publication Date | 2018 |
Publication Name | Proceedings - International Conference on Image Processing, ICIP |
Resource | Scopus |
ISSN | 15224880 |
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. |
Language | en |
Publisher | IEEE Computer Society |
Subject | Approximate kernel-based learning Approximate Nearest Neighbor Search Hashing Low-rank Approximation Neural Network Acceleration Vector Quantization |
Type | Conference Paper |
Pagination | 311 - 315 |
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