Ye Y Latent Factor Analysis for High-dimensional and Sparse Matrices 2022

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[b]Ye Y Latent Factor Analysis for High-dimensional and Sparse Matrices 2022 | 3.98 MB[/b]
[b]English | 99 Pages[/b]
[b]Title:[/b] Latent Factor Analysis for High-dimensional and Sparse Matrices: A particle swarm optimization-based approach (SpringerBriefs in Computer Science)
[b]Author:[/b] Ye Yuan
[b]Year:[/b] 2022

[b]Description:[/b]
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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