# Automatic Hyperparameter Tuning in Sparse Matrix Factorization

arXiv: https://arxiv.org/abs/2305.10114 · HTML: https://mlsanity.com/paper/2305.10114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 6 of 6)

- [Phase transitions and sample complexity in Bayes-optimal matrix factorization](https://mlsanity.com/paper/1402.1298/) (2014)
- [Phase Transitions in Sparse PCA](https://mlsanity.com/paper/1503.00338/) (2015)
- [Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions](https://mlsanity.com/paper/1807.02694/) (2018)
- [Phase Diagram and Approximate Message Passing for Blind Calibration and Dictionary Learning](https://mlsanity.com/paper/1301.5898/) (2013)
- [Consistent Parameter Estimation for LASSO and Approximate Message Passing](https://mlsanity.com/paper/1511.01017/) (2015)
- [Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior](https://mlsanity.com/paper/1803.06234/) (2018)

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

- [L1-Regularized ICA: A Novel Method for Analysis of Task-related fMRI Data](https://mlsanity.com/paper/2410.13171/) (2024)

## Abstract (arXiv)

We study the problem of hyperparameter tuning in sparse matrix factorization under Bayesian framework. In the prior work, an analytical solution of sparse matrix factorization with Laplace prior was obtained by variational Bayes method under several approximations. Based on this solution, we propose a novel numerical method of hyperparameter tuning by evaluating the zero point of normalization factor in sparse matrix prior. We also verify that our method shows excellent performance for ground-truth sparse matrix reconstruction by comparing it with the widely-used algorithm of sparse principal component analysis.