# Tighter Low-rank Approximation via Sampling the Leveraged Element

arXiv: https://arxiv.org/abs/1410.3886 · HTML: https://mlsanity.com/paper/1410.3886/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2014.

## Built on by (most cited first, 3 of 3)

- [When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?](https://mlsanity.com/paper/2502.16912/) (2025)
- [Estimating leverage scores via rank revealing methods and randomization](https://mlsanity.com/paper/2105.11004/) (2021)
- [An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration](https://mlsanity.com/paper/2109.11703/) (2021)

## Abstract (arXiv)

In this work, we propose a new randomized algorithm for computing a low-rank approximation to a given matrix. Taking an approach different from existing literature, our method first involves a specific biased sampling, with an element being chosen based on the leverage scores of its row and column, and then involves weighted alternating minimization over the factored form of the intended low-rank matrix, to minimize error only on these samples. Our method can leverage input sparsity, yet produce approximations in em spectral (as opposed to the weaker Frobenius) norm; this combines the best aspects of otherwise disparate current results, but with a dependence on the condition number kappa = σ1/σr. In particular we require O(nnz(M) + fracnkappa2 r5ε2) computations to generate a rank-r approximation to M in spectral norm. In contrast, the best existing method requires O(nnz(M)+ fracnr2ε4) time to compute an approximation in Frobenius norm. Besides the tightness in spectral norm, we have a better dependence on the error ε. Our method is naturally and highly parallelizable. Our new approach enables two extensions that are interesting on their own. The first is a new method to directly compute a low-rank approximation (in efficient factored form) to the product of two given matrices; it computes a small random set of entries of the product, and then executes weighted alternating minimization (as before) on these. The sampling strategy is different because now we cannot access leverage scores of the product matrix (but instead have to work with input matrices). The second extension is an improved algorithm with smaller communication complexity for the distributed PCA setting (where each server has small set of rows of the matrix, and want to compute low rank approximation with small amount of communication with other servers).