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Fast and Memory-Efficient Algorithms for High-Order Tucker Decomposition

Title
Fast and Memory-Efficient Algorithms for High-Order Tucker Decomposition
Authors
Hwanjo YuJiyuan ZhangJinoh OhKijung ShinEvangelos E. PapalexakisChristos Faloutsos
Date Issued
Jan-2020
Publisher
Springer
Abstract
Multi-aspect data appear frequently in web-related applications. For example, product reviews are quadruplets of the form (user, product, keyword, timestamp), and search-engine logs are quadruplets of the form (user, keyword, location, timestamp). How can we analyze such web-scale multi-aspect data on an off-the-shelf workstation with a limited amount of memory? Tucker decomposition has been used widely for discovering patterns in such multi-aspect data, which are naturally expressed as large but sparse tensors. However, existing Tucker decomposition algorithms have limited scalability, failing to decompose large-scale high-order (
URI
http://oasis.postech.ac.kr/handle/2014.oak/100877
ISSN
0219-1377
Article Type
Article
Citation
Knowledge and Information Systems, 2020-01
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 YU, HWANJO
Dept of Computer Science & Enginrg
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