Proportional-Integral-Derivative-Incorporated Latent Factorization of Tensors for Large-Scale Dynamic Network Analysis-Reference-Cited by-同舟云学术

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Proportional-Integral-Derivative-Incorporated Latent Factorization of Tensors for Large-Scale Dynamic Network Analysis

Published:2021-10-22 Issue: Volume: Page:
ISSN:
Container-title:2021 China Automation Congress (CAC)
language:
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Author:

Wu Hao¹,Xia Yan²,Luo Xin¹

Affiliation:

1. University of Chinese Academy of Sciences,Chinese Academy of Sciences,Chongqing Institute of Green and Intelligent Technology,Chongqing,China

2. Chongqing Aerospace Polytechnic,School of Electronic Information and Communication Engineering,Chongqing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9727224/9727236/09727662.pdf?arnumber=9727662

Reference28 articles.

1. Advancing Non-negative Latent Factorization of Tensors with Diversified Regularizations

2. A Survey on Tensor Techniques and Applications in Machine Learning

3. Scalable Tensor Factorizations with Missing Data

4. Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors

5. A Fused CP Factorization Method for Incomplete Tensors

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. RNL: A Robust and Highly-Efficient Model for Time-Aware Web Service QoS Prediction;Lecture Notes in Computer Science;2023

2. Diverse Biases Nonnegative Latent Factorization of Tensors;Dynamic Network Representation Based on Latent Factorization of Tensors;2023

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