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VEST: Very sparse tucker factorization of large-scale tensorsoa mark
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Publication Year
2021-01-01
Journal
Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021, pp.172-179
Keyword
Scalable tensor factorizationSparsityTucker
Mesh Keyword
Coordinate descentCore tensorCurrent decompositionReal life datasetsScalability issueSparsity ratiosTensor factorizationTrade off
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Science ApplicationsComputer Vision and Pattern RecognitionInformation SystemsSignal ProcessingInformation Systems and Management
Abstract
Given a large tensor, how can we decompose it to sparse core tensor and factor matrices without reducing the accuracy? Existing approaches either output dense results or have scalability issues. In this paper, we propose VEST, a tensor factorization method for large partially observable data to output a very sparse core tensor and factor matrices. VEST performs initial decomposition and iteratively determines unimportant entries in the decomposition results, removes the unimportant entries, and updates the remaining entries. To determine unimportant entries of factor matrices and core tensor, we define and use the entry-wise 'responsibility' of the current decomposition. For scalable computation, the entries are updated iteratively using a carefully derived coordinate descent rule in parallel. Also, VEST automatically searches for the best sparsity ratio that results in a balanced trade-off between sparsity and accuracy. Extensive experiments show that our method VEST produces more accurate results compared to the best performing competitors for all tested real-life datasets.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36671
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102974603&origin=inward
DOI
https://doi.org/10.1109/bigcomp51126.2021.00041
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9373068
Type
Conference
Funding
Publication of this article has been funded by the National Research Foundation of Korea (2018R1A1A3A0407953, 2018R1A5A1060031) and by Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (19CTAP-C152020, 19CTAP-C152017).
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Lee, Sael이슬
Department of Software and Computer Engineering
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