Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data

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Title
Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data
Authors
Keywords
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Journal
IEEE Transactions on Automation Science and Engineering
Volume 18, Issue 4, Pages 2142-2155
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Online
2021-01-14
DOI
10.1109/tase.2020.3040400

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