Latent Factor Analysis for High-Dimensional and Sparse Matrices: A Particle Swarm Optimization-Based Approach
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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.
Format:Paperback
Language:English
ISBN:9811967024
ISBN13:9789811967023
Release Date:November 2022
Publisher:Springer
Length:92 Pages
Weight:0.34 lbs.
Dimensions:0.2" x 6.1" x 9.2"
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