Springer, 2022. — 99 p. — ISBN 978-981-19-6702-3.
Анализ скрытых факторов для многомерных и разреженных матриц: подход, основанный на оптимизации роя частиц
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.
High-dimensional and sparse (HiDS) matrices are commonly seen in many big data-related industrial applications like electronic commerce, cloud services, social networks, and wireless sensor networks. Despite its extreme sparse, an HiDS matrix contains rich knowledge regarding desired patterns like users’ potential favorites, item clusters, and topological neighbors. Hence, how to efficiently and accurately extract desired knowledge from it for various data analysis tasks becomes a highly interesting issue.
Latent factor analysis (LFA) has proven to be highly efficient for addressing an HiDS matrix owing to its high scalability and efficiency. However, an LFA model’s performance relies heavily on its hyper-parameters, which should be chosen with care. The common strategy is to employ grid-search to tune these hyper-parameters. However, it requires expensive computational cost to search the whole candidate space, especially when building an LFA model on an HiDS matrix. Hence, how to implement a hyper-parameter-free LFA model becomes a significant issue.
Learning Rate-Free Latent Factor Analysis via PSO
Learning Rate and Regularization Coefficient-Free Latent Factor Analysis via PSO
Regularization and Momentum Coefficient-Free Non-negative Latent Factor Analysis via PSO
Advanced Learning Rate-Free Latent Factor Analysis via P2 SO
Conclusion and Future Directions