# Streaming Coresets for Symmetric Tensor Factorization

arXiv: https://arxiv.org/abs/2006.01225 · HTML: https://mlsanity.com/paper/2006.01225/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Oblivious sketching for logistic regression](https://mlsanity.com/paper/2107.06615/) (2021)
- [p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets](https://mlsanity.com/paper/2203.13568/) (2022)
- [Turnstile ℓp leverage score sampling with applications](https://mlsanity.com/paper/2406.00339/) (2024)
- [Accurate Coresets for Latent Variable Models and Regularized Regression](https://mlsanity.com/paper/2412.20189/) (2024)
- [Efficient NTK using Dimensionality Reduction](https://mlsanity.com/paper/2210.04807/) (2022)
- [Computing Approximate ℓp Sensitivities](https://mlsanity.com/paper/2311.04158/) (2023)
- [Accelerating Large-Scale Regularized High-Order Tensor Recovery](https://mlsanity.com/paper/2506.09594/) (2025)
- [Deterministic Coreset for Lp Subspace](https://mlsanity.com/paper/2601.00361/) (2026)
- [COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs](https://mlsanity.com/paper/2605.14900/) (2026)

## Abstract (arXiv)

Factorizing tensors has recently become an important optimization module in a number of machine learning pipelines, especially in latent variable models. We show how to do this efficiently in the streaming setting. Given a set of n vectors, each in ℝd, we present algorithms to select a sublinear number of these vectors as coreset, while guaranteeing that the CP decomposition of the p-moment tensor of the coreset approximates the corresponding decomposition of the p-moment tensor computed from the full data. We introduce two novel algorithmic techniques: online filtering and kernelization. Using these two, we present six algorithms that achieve different tradeoffs of coreset size, update time and working space, beating or matching various state of the art algorithms. In the case of matrices (2-ordered tensor), our online row sampling algorithm guarantees (1 ± ε) relative error spectral approximation. We show applications of our algorithms in learning single topic modeling.