# Faster Matrix Completion Using Randomized SVD

arXiv: https://arxiv.org/abs/1810.06860 · HTML: https://mlsanity.com/paper/1810.06860/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [Efficient GPU implementation of randomized SVD and its applications](https://mlsanity.com/paper/2110.03423/) (2021)
- [Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference](https://mlsanity.com/paper/2203.10044/) (2022)
- [SVD-AE: Simple Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/2405.04746/) (2024)
- [Large-Scale System Identification Using a Randomized SVD](https://mlsanity.com/paper/2109.02703/) (2021)
- [Q&C: When Quantization Meets Cache in Efficient Image Generation](https://mlsanity.com/paper/2503.02508/) (2025)

## Abstract (arXiv)

Matrix completion is a widely used technique for image inpainting and personalized recommender system, etc. In this work, we focus on accelerating the matrix completion using faster randomized singular value decomposition (rSVD). Firstly, two fast randomized algorithms (rSVD-PI and rSVD- BKI) are proposed for handling sparse matrix. They make use of an eigSVD procedure and several accelerating skills. Then, with the rSVD-BKI algorithm and a new subspace recycling technique, we accelerate the singular value thresholding (SVT) method in [1] to realize faster matrix completion. Experiments show that the proposed rSVD algorithms can be 6X faster than the basic rSVD algorithm [2] while keeping same accuracy. For image inpainting and movie-rating estimation problems, the proposed accelerated SVT algorithm consumes 15X and 8X less CPU time than the methods using svds and lansvd respectively, without loss of accuracy.