Abstract
Low-rank tensor decomposition has many applications in signal processing and machine learning, and is becoming increasingly important for analyzing big data. A significant challenge is the computation of intermediate products which can be much larger than the final result of the computation, or even the original tensor. We propose a scheme that allows memory-efficient in-place updates of intermediate matrices. Motivated by recent advances in big tensor decomposition from multiple compressed replicas, we also consider the related problem of memory-efficient tensor compression. The resulting algorithms can be parallelized, and can exploit but do not require sparsity.
| Original language | English (US) |
|---|---|
| Title of host publication | Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2014 |
| Editors | Michael B. Matthews |
| Publisher | IEEE Computer Society |
| Pages | 581-585 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479982974 |
| DOIs | |
| State | Published - Apr 24 2015 |
| Event | 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2014 - Pacific Grove, United States Duration: Nov 2 2014 → Nov 5 2014 |
Publication series
| Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
|---|---|
| Volume | 2015-April |
| ISSN (Print) | 1058-6393 |
| ISSN (Electronic) | 2576-2303 |
Conference
| Conference | 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2014 |
|---|---|
| Country/Territory | United States |
| City | Pacific Grove |
| Period | 11/2/14 → 11/5/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
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