Latent Factor Analysis for High-dimensional and Sparse Matrices by Ye Yuan
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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Concise Computer Mathematics
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The Data Grid
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Resource Management for Satellite-Terrestrial Vehicular Networks
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Computational Intelligence for Remote Sensing Image Change Detection
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Algorithmic Fairness in AI-Mediated Institutional Communication
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AbstractSwarm Multi-Agent Modeling and Simulation
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Federated Learning for Smart Mobility
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Introduction to Ethical Software Development
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Digital Image Forgery Detection
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Blockchain Without Barriers
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Human Reconstruction Using mmWave Technology
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Universal Time-Series Forecasting with Mixture Predictors
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Turbo Message Passing Algorithms for Structured Signal Recovery
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Developing Sustainable and Energy-Efficient Software Systems
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Cognitive Security
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Dynamic Network Representation Based on Latent Factorization of Tensors
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Security-aware Cooperation in Cognitive Radio Networks
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Robust Latent Feature Learning for Incomplete Big Data
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Seamless and Secure Communications over Heterogeneous Wireless Networks
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The AI Act and The Agile Safety Plan
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Representation in Machine Learning
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Enabling Secure and Privacy Preserving Communications in Smart Grids
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Energy Detection for Spectrum Sensing in Cognitive Radio
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Enabling Content Distribution in Vehicular Ad Hoc Networks
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Machine Learning Empowered Intelligent Data Center Networking
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Energy-Efficient Area Coverage for Intruder Detection in Sensor Networks
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.
| SKU | Unavailable |
| ISBN 13 | 9789811967023 |
| ISBN 10 | 9811967024 |
| Title | Latent Factor Analysis for High-dimensional and Sparse Matrices |
| Author | Ye Yuan |
| Series | Springerbriefs In Computer Science |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer Verlag, Singapore |
| Year published | 2022-11-16 |
| Number of pages | 92 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |