Latent factor analysis for high-dimensional and sparse matrices [electronic resource] : a particle swarm optimization-based approach / Ye Yuan, Xin Luo.

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Latent factor analysis for high-dimensional and sparse matrices [electronic resource] : a particle swarm optimization-based approach / Ye Yuan, Xin Luo.

9789811967030 (electronic bk.)
9811967032 (electronic bk.)
9789811967023
9811967024

Singapore : Springer, 2022.

English

1 online resource (99 pages).

10.1007/978-981-19-6703-0 doi

Q337.3

006.3/824

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.

Includes bibliographical references.

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Online resource; title from PDF title page (SpringerLink, viewed November 29, 2022).

SpringerBriefs in computer science.

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