Latent Factor Analysis for High-Dimensional and Sparse Matrices: A Particle Swarm Optimization-Based Approach

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.