EAN: 9789811967023

SPRINGERBRIEFS IN COMPUTER SCIENCE LATENT FAC...

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.

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