Latent Factor Analysis for High-dimensional and Sparse Matrices [electronic resource] : A particle swarm optimization-based approach /

Chapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion.

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.