书目名称 | Latent Factor Analysis for High-dimensional and Sparse Matrices |
副标题 | A particle swarm opt |
编辑 | Ye Yuan,Xin Luo |
视频video | http://file.papertrans.cn/582/581797/581797.mp4 |
概述 | Offers a comprehensive introduction to latent factor analysis on high-dimensional and sparse data.Presents an effective hyper-parameter adaptation method for latent factor analysis models.Outlines an |
丛书名称 | SpringerBriefs in Computer Science |
图书封面 |  |
描述 | 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.. |
出版日期 | Book 2022 |
关键词 | Latent factor analysis; High-dimensional and Sparse; Hyper-parameter-free; Particle Swarm Optimization; |
版次 | 1 |
doi | https://doi.org/10.1007/978-981-19-6703-0 |
isbn_softcover | 978-981-19-6702-3 |
isbn_ebook | 978-981-19-6703-0Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
issn_series | 2191-5768 |
copyright | The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 |