# Accelerating Block Coordinate Descent for Nonnegative Tensor Factorization

arXiv: https://arxiv.org/abs/2001.04321 · HTML: https://mlsanity.com/paper/2001.04321/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and Applications](https://mlsanity.com/paper/2209.12638/) (2022)
- [Representation Theorem for Matrix Product States](https://mlsanity.com/paper/2103.08277/) (2021)

## Abstract (arXiv)

This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an extrapolation strategy in-between block updates, referred to as heuristic extrapolation with restarts (HER). HER significantly accelerates the empirical convergence speed of most existing block-coordinate algorithms for dense NTF, in particular for challenging computational scenarios, while requiring a negligible additional computational budget.