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Latent factor analysis for high-dime...

Yuan, Ye.

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Latent factor analysis for high-dimensional and sparse matrices = a particle swarm optimization-based approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Latent factor analysis for high-dimensional and sparse matrices/ by Ye Yuan, Xin Luo.
Reminder of title:	a particle swarm optimization-based approach /
Author:	Yuan, Ye.
other author:	Luo, Xin.
Published:	Singapore :Springer Nature Singapore : : 2022.,
Description:	viii, 92 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion.
Contained By:	Springer Nature eBook
Subject:	Swarm intelligence. -
Online resource:	https://doi.org/10.1007/978-981-19-6703-0
ISBN:	9789811967030

Latent factor analysis for high-dimensional and sparse matrices = a particle swarm optimization-based approach /
Yuan, Ye.

Latent factor analysis for high-dimensional and sparse matricesa particle swarm optimization-based approach /[electronic resource] :by Ye Yuan, Xin Luo. - Singapore :Springer Nature Singapore :2022. - viii, 92 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Chapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion.

Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.

ISBN: 9789811967030

Standard No.: 10.1007/978-981-19-6703-0doiSubjects--Topical Terms:

577800
Swarm intelligence.

LC Class. No.: Q337.3

Dewey Class. No.: 006.3824

Latent factor analysis for high-dimensional and sparse matrices = a particle swarm optimization-based approach /
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505 0 $a Chapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion.
520 $a Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.
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