# Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior

arXiv: https://arxiv.org/abs/1803.06234 · HTML: https://mlsanity.com/paper/1803.06234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Automatic Hyperparameter Tuning in Sparse Matrix Factorization](https://mlsanity.com/paper/2305.10114/) (2023)

## Abstract (arXiv)

We derive analytical expression of matrix factorization/completion solution by variational Bayes method, under the assumption that observed matrix is originally the product of low-rank dense and sparse matrices with additive noise. We assume the prior of sparse matrix is Laplace distribution by taking matrix sparsity into consideration. Then we use several approximations for derivation of matrix factorization/completion solution. By our solution, we also numerically evaluate the performance of sparse matrix reconstruction in matrix factorization, and completion of missing matrix element in matrix completion.