# Dictionary Learning for Massive Matrix Factorization

arXiv: https://arxiv.org/abs/1605.00937 · HTML: https://mlsanity.com/paper/1605.00937/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2016.

## Built on by (most cited first, 4 of 4)

- [Tensor Methods in Computer Vision and Deep Learning](https://mlsanity.com/paper/2107.03436/) (2021)
- [In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting](https://mlsanity.com/paper/2409.08771/) (2024)
- [Lightweight Conceptual Dictionary Learning for Text Classification Using Information Compression](https://mlsanity.com/paper/2405.01584/) (2024)
- [Quantum Semi-Random Forests for Qubit-Efficient Recommender Systems](https://mlsanity.com/paper/2508.00027/) (2025)

## Abstract (arXiv)

Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising. Its applicability to large datasets has been addressed with online and randomized methods, that reduce the complexity in one of the matrix dimension, but not in both of them. In this paper, we tackle very large matrices in both dimensions. We propose a new factoriza-tion method that scales gracefully to terabyte-scale datasets, that could not be processed by previous algorithms in a reasonable amount of time. We demonstrate the efficiency of our approach on massive functional Magnetic Resonance Imaging (fMRI) data, and on matrix completion problems for recommender systems, where we obtain significant speed-ups compared to state-of-the art coordinate descent methods.