MFAI : A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 1339–1349 |
Number of pages | 12 |
Journal / Publication | Journal of Computational and Graphical Statistics |
Volume | 33 |
Issue number | 4 |
Online published | 25 Mar 2024 |
Publication status | Published - 2024 |
Link(s)
Abstract
In various practical situations, matrix factorization methods suffer from poor data quality, such as high data sparsity and low signal-to-noise ratio (SNR). Here, we consider a matrix factorization problem by using auxiliary information, which is massively available in real-world applications, to overcome the challenges caused by poor data quality. Unlike existing methods that mainly rely on simple linear models to combine auxiliary information with the main data matrix, we propose to integrate gradient boosted trees in the probabilistic matrix factorization framework to effectively leverage auxiliary information (MFAI). Thus, MFAI naturally inherits several salient features of gradient boosted trees, such as the capability of flexibly modeling nonlinear relationships and robustness to irrelevant features and missing values in auxiliary information. The parameters in MFAI can be automatically determined under the empirical Bayes framework, making it adaptive to the utilization of auxiliary information and immune to overfitting. Moreover, MFAI is computationally efficient and scalable to large datasets by exploiting variational inference. We demonstrate the advantages of MFAI through comprehensive numerical results from simulation studies and real data analyses. Our approach is implemented in the R package mfair available at https://github.com/YangLabHKUST/mfair. Supplementary materials for this article are available online. © 2024 American Statistical Association and Institute of Mathematical Statistics.
Research Area(s)
- Empirical Bayes, Gradient boosting, Human brain gene expression, Low-rank, Matrix completion, Movie rating
Citation Format(s)
MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information. / Wang, Zhiwei; Zhang, Fa; Zheng, Cong et al.
In: Journal of Computational and Graphical Statistics, Vol. 33, No. 4, 2024, p. 1339–1349.
In: Journal of Computational and Graphical Statistics, Vol. 33, No. 4, 2024, p. 1339–1349.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review