Latent Factor Analysis for High-dimensional and Sparse Matrices : A particle swarm optimization-based approach

Name: Latent Factor Analysis for High-dimensional and Sparse Matrices
Brand: Springer Verlag, Singapore
SKU: 9789811967023
Price: 7200 KES
Availability: OutOfStock
ISBN: 9789811967023

1st ed. 2022

by Xin Luo , Ye Yuan

Book Details

Format Paperback / softback

Book Series SpringerBriefs in Computer Science

ISBN-10 9811967024

ISBN-13 9789811967023

Edition 1st ed. 2022

Publisher Springer Verlag, Singapore

Imprint Springer Verlag, Singapore

Country of Manufacture GB

Country of Publication GB

Publication Date Nov 16th, 2022

Print length 92 Pages

Product Classification: Probability & statistics Probability and statistics Algorithms & data structures Algorithms and data structures Data mining Machine learning

KSh 7,200.00

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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.

Get Latent Factor Analysis for High-dimensional and Sparse Matrices by Xin Luo, Ye Yuan at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by Springer Verlag, Singapore and it has 92 pages.