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Latent Factor Analysis for High-dimensional and Sparse Matrices

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Latent Factor Analysis for High-dimensional and Sparse Matrices
Latent Factor Analysis for High-dimensional and Sparse Matrices:
A particle swarm optimization-based approach

English | 2022 | ISBN: 9811967024 | 174 Pages | PDF EPUB (True) | 21 MB


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.



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