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Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations

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
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/DCC47342.2020.00045 DOIArticle
https://arxiv.org/abs/1901.11261arXivDiscussion Paper
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.
Funders:
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-15-1-0221
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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