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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 9; H. 5; S. 3582 - 3597
Hauptverfasser:	Li, Jinli, Yuan, Ye, Luo, Xin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!