Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers

In Big Data-based applications, high-dimensional and incomplete (HDI) data are frequently used to represent the complicated interactions among numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model can process such data efficiently. Unfortunately, a standard SGD...

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Vydáno v:	IEEE transactions on emerging topics in computational intelligence Ročník 9; číslo 5; s. 3582 - 3597
Hlavní autoři:	Li, Jinli, Yuan, Ye, Luo, Xin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Adaptation models Big Data Computational modeling Controllers Discriminant analysis ensemble Factor analysis Feedback control High-dimensional and incomplete data latent factor analysis Learning Machine learning algorithms Missing data Optimization parameter adaptation Particle swarm optimization Prediction algorithms Predictive models Proportional integral derivative Stochastic processes
ISSN:	2471-285X, 2471-285X
On-line přístup:	Získat plný text
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