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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.
| Autorius: | Xin Luo, Ye Yuan, |
| Serija: | SpringerBriefs in Computer Science |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2022 |
| Knygos puslapių skaičius: | 100 |
| ISBN-10: | 9811967024 |
| ISBN-13: | 9789811967023 |
| Formatas: | 235 x 155 x 6 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Latent Factor Analysis for High-dimensional and Sparse Matrices: A particle swarm optimization-based approach“
