國立虎尾科技大學 |

Latent Factor Analysis for High-dimensional and Sparse Matrices = A particle swarm optimization-based approach /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Latent Factor Analysis for High-dimensional and Sparse Matrices/ by Ye Yuan, Xin Luo.
其他題名:	A particle swarm optimization-based approach /
作者:	Yuan, Ye.
其他作者:	Luo, Xin.
面頁冊數:	VIII, 92 p. 1 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Data Mining and Knowledge Discovery. -
電子資源:	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. - 1st ed. 2022. - VIII, 92 p. 1 illus.online resource. - 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:

677765
Data Mining and Knowledge Discovery.

LC Class. No.: Q336

Dewey Class. No.: 005.7

Latent Factor Analysis for High-dimensional and Sparse Matrices = A particle swarm optimization-based approach /
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