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Accurate Online Tensor Factorization for Temporal Tensor Streams with Missing Values

Published: 30 October 2021 Publication History

Abstract

Given a time-evolving tensor stream with missing values, how can we accurately discover latent factors in an online manner to predict missing values? Online tensor factorization is a crucial task with many important applications including the analysis of climate, network traffic, and epidemic disease. However, existing online methods have disregarded temporal locality and thus have limited accuracy.
In this paper, we propose STF (Streaming Tensor Factorization), an accurate online tensor factorization method for real-world temporal tensor streams with missing values. We exploit an attention-based temporal regularization to learn inherent temporal patterns of the streams. We also propose an efficient online learning algorithm which allows each row of the temporal factor matrix to be updated from past and future information. Extensive experiments show that the proposed method gives the state-of-the-art accuracy, and quickly processes each tensor slice.

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  • (2024)Accurate Coupled Tensor Factorization with Knowledge Graph2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825614(1009-1018)Online publication date: 15-Dec-2024
  • (2024)Stochastic Low-Rank Tensor Bandits for Multi-Dimensional Online Decision MakingJournal of the American Statistical Association10.1080/01621459.2024.2311364(1-14)Online publication date: 7-Mar-2024
  • (2024)Scale-variant structural feature construction of EEG stream via component-increased Dynamic Tensor DecompositionKnowledge-Based Systems10.1016/j.knosys.2024.111747294(111747)Online publication date: Jun-2024
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    cover image ACM Conferences
    CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
    October 2021
    4966 pages
    ISBN:9781450384469
    DOI:10.1145/3459637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 30 October 2021

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    Author Tags

    1. attention-based temporal regularization
    2. online tensor factorization
    3. tensor streams

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    Cited By

    View all
    • (2024)Accurate Coupled Tensor Factorization with Knowledge Graph2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825614(1009-1018)Online publication date: 15-Dec-2024
    • (2024)Stochastic Low-Rank Tensor Bandits for Multi-Dimensional Online Decision MakingJournal of the American Statistical Association10.1080/01621459.2024.2311364(1-14)Online publication date: 7-Mar-2024
    • (2024)Scale-variant structural feature construction of EEG stream via component-increased Dynamic Tensor DecompositionKnowledge-Based Systems10.1016/j.knosys.2024.111747294(111747)Online publication date: Jun-2024
    • (2023)Static and Streaming Tucker Decomposition for Dense TensorsACM Transactions on Knowledge Discovery from Data10.1145/356868217:5(1-34)Online publication date: 27-Feb-2023
    • (2022)A Contemporary and Comprehensive Survey on Streaming Tensor DecompositionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323087435:11(10897-10921)Online publication date: 20-Dec-2022
    • (2022)DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00229(2454-2467)Online publication date: May-2022

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