Latent Factor Analysis for High-dimensional and Sparse Matrices
A particle swarm optimization-based approach
(Sprache: Englisch)
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...
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Klappentext zu „Latent Factor Analysis for High-dimensional and Sparse Matrices “
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.
Inhaltsverzeichnis zu „Latent Factor Analysis for High-dimensional and Sparse Matrices “
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.
Autoren-Porträt von Ye Yuan, Xin Luo
Dr. Ye Yuan is an Associate Professor at the College of Computer and Information Science, Southwest University. His main research fields are data mining and machine learning. He has published over 24 SCI/EI papers, including for top journals and conferences like IEEE T. KDE, CYB, WWW and ECAI. He has applied for 11 and holds 5 national invention patents and won First Prize in the Wu Wenjun AI Science and Technology Progress Award and First Prize in the Chongqing Science and Technology Progress Award.Dr. Xin Luo is a Professor at the College of Computer and Information Science, Southwest University. His current research interests include machine intelligence, big data, and cloud computing. He has published over 200 papers (including over 87 IEEE TRANSACTIONS papers and 17 highly cited papers in ESI) in the above areas. He holds 35 national invention patents. He was part of the Pioneer Hundred Talents Program of the Chinese Academy of Sciences in 2016, the Advanced Support of the Pioneer Hundred Talents Program of Chinese Academy of Sciences in 2018, and the National High-Level Talents Special Support Program in 2020. He won First Prize in the Chongqing Natural Science Award (2019), First Prize in the Wu Wenjun AI Science and Technology Progress Award (2018) and First Prize in the Chongqing Science and Technology Progress Award (2018). He serves as an Associate Editor for the IEEE/CAA Journal of Automatica Sinica, and for IEEE Transactions on Neural Networks and Learning Systems. He received the Outstanding Associate Editor Award from the IEEE/CAA Journal of Automatica Sinica in 2020.
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