Latent Factor Analysis for High-dimensional and Sparse Matrices: A particle swarm optimization-based approach

di Ye Yuan (Autore)

Xin Luo (Autore)

Springer Verlag, Singapore, 2022

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.

• Autore: Ye Yuan, Xin Luo
• Editore: Springer Verlag, Singapore
• Collana: SpringerBriefs in Computer Science
• Anno: 2022
• Rilegatura: Paperback / softback
• Pagine: 92 p.
• Dimensioni: 235 x 155 mm
• EAN: 9789811967023

Testo in English