Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent.

sgdGMF

An R package for efficient estimation of generalized matrix factorization (GMF) models [1,2,3]. The package implements the adaptive stochastic gradient descent with block- and coordinate-wise sub-sampling strategies proposed in [4]. Additionally, sgdGMF implements the alternated iterative re-weighted least squares [1,3] and diagonal-Hessian quasi-Newton [1] algorithms.

References

[1] Collins, M., Dasgupta, S., Schapire, R.E. (2001). A generalization of principal components analysis to the exponential family. Advances in neural information processing systems, 14.

[2] Kidzinski, L., Hui, F.K.C., Warton, D.I., Hastie, T.J. (2022). Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays. Journal of Machine Learning Research, 23(291): 1--29.

[3] Wang, L., Carvalho, L. (2023). Deviance matrix factorization. Electronic Journal of Statistics, 17(2): 3762--3810.

[4] Castiglione, C., Segers, A., Clement, L, Risso, D. (2024). Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data. arXiv preprint: arXiv:2412.20509.