Latent Factor Analysis for High-dimensional and Sparse Matrices A particle swarm optimization-based approach /
Yuan, Ye.
Latent Factor Analysis for High-dimensional and Sparse Matrices A particle swarm optimization-based approach / [electronic resource] : by Ye Yuan, Xin Luo. - 1st ed. 2022. - VIII, 92 p. 1 illus. online resource. - SpringerBriefs in Computer Science, 2191-5776 . - SpringerBriefs in Computer Science, .
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.
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.
9789811967030
10.1007/978-981-19-6703-0 doi
Artificial intelligence--Data processing.
Quantitative research.
Data mining.
Data Science.
Data Analysis and Big Data.
Data Mining and Knowledge Discovery.
Q336
005.7
Latent Factor Analysis for High-dimensional and Sparse Matrices A particle swarm optimization-based approach / [electronic resource] : by Ye Yuan, Xin Luo. - 1st ed. 2022. - VIII, 92 p. 1 illus. online resource. - SpringerBriefs in Computer Science, 2191-5776 . - SpringerBriefs in Computer Science, .
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.
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.
9789811967030
10.1007/978-981-19-6703-0 doi
Artificial intelligence--Data processing.
Quantitative research.
Data mining.
Data Science.
Data Analysis and Big Data.
Data Mining and Knowledge Discovery.
Q336
005.7