SpringerBriefs in Computer Science Latent Factor Analysis for High-dimensional and Sparse Matrices - Ye Yuan Xin Luo Kartoniert (TB)

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 th e 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.