# Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation

arXiv: https://arxiv.org/abs/2109.09534 · HTML: https://mlsanity.com/paper/2109.09534/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [DFacTo: Distributed Factorization of Tensors](https://mlsanity.com/paper/1406.4519/) (2014)
- [Parallel Nonnegative CP Decomposition of Dense Tensors](https://mlsanity.com/paper/1806.07985/) (2018)
- [Distributed Methods for High-dimensional and Large-scale Tensor Factorization](https://mlsanity.com/paper/1410.5209/) (2014)

## Abstract (arXiv)

We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a stochastic variation of the accelerated gradient algorithm. We experimentally test the effectiveness and the efficiency of our algorithm using both real-world and synthetic data. We develop a shared-memory implementation of our algorithm using the multi-threaded API OpenMP, which attains significant speedup. We believe that our approach is a very competitive candidate for the solution of very large nonnegative tensor completion problems.