Shi, Yang and Anandkumar, Animashree (2020) Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations. In: 2020 Data Compression Conference (DCC). IEEE , Piscataway, NJ, p. 394. ISBN 978-1-7281-6457-1. https://resolver.caltech.edu/CaltechAUTHORS:20190327-085821224
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Abstract
Sketching is a randomized dimensionalityreduction method that aims to preserve relevant information in large-scale datasets. In this paper, we propose a novel extension known as Higher-order Count Sketch (HCS). We derive efficient (approximate) computation of various tensor operations such as tensor products and tensor contractions directly on the sketched data. HCS is the first sketch to fully exploit the multi-dimensional nature of higher-order tensors.
| Item Type: | Book Section | |||||||||
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| Alternate Title: | Multi-dimensional Tensor Sketch, Multi-dimensional Tensor Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations | |||||||||
| Additional Information: | © 2020 IEEE. This paper is supported by AFOSR Grant FA9550-15-1-0221. | |||||||||
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| Record Number: | CaltechAUTHORS:20190327-085821224 | |||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190327-085821224 | |||||||||
| Official Citation: | Y. Shi and A. Anandkumar, "Higher-Order Count Sketch: Dimensionality Reduction that Retains Efficient Tensor Operations," 2020 Data Compression Conference (DCC), Snowbird, UT, USA, 2020, pp. 394-394, doi: 10.1109/DCC47342.2020.00045 | |||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
| ID Code: | 94183 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | George Porter | |||||||||
| Deposited On: | 28 Mar 2019 15:10 | |||||||||
| Last Modified: | 04 Jun 2020 23:02 |
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