Latent factor analysis for high-dimensional and sparse matrices : a particle swarm optimization-based approach / Ye Yuan, Xin Luo.
"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-param...
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| Main Authors: | , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Singapore :
Springer Nature Singapore,
2022.
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| Series: | SpringerBriefs in computer science.
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| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | "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."--Publisher's website. |
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| Physical Description: | 1 online resource : illustrations. |
| ISBN: | 9811967024 9789811967023 9811967032 9789811967030 |