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-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.
Kateqoriyalar:
İl:
2022
Nəşriyyat:
Springer
Dil:
english
Səhifələr:
98
ISBN 10:
9811967024
ISBN 13:
9789811967023
Seriyalar:
SpringerBriefs in Computer Science
Fayl:
PDF, 3.98 MB
IPFS:
,
english, 2022
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