# An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model

arXiv: https://arxiv.org/abs/2204.04843 · HTML: https://mlsanity.com/paper/2204.04843/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 65 references that are in the map.
- First posted on arXiv 11 Apr 2022.

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

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Integrating Hypertension Phenotype and Genotype with Hybrid Non-negative Matrix Factorization](https://mlsanity.com/paper/1805.05008/) (2018)
- [Piano Transcription in the Studio Using an Extensible Alternating Directions Framework](https://mlsanity.com/paper/1606.00785/) (2016)

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

- [Multi-Head Self-Attending Neural Tucker Factorization](https://mlsanity.com/paper/2501.09776/) (2025)
- [Neural Canonical Polyadic Factorization for Traffic Analysis](https://mlsanity.com/paper/2506.15079/) (2025)
- [Spectra-Guided Neural Tucker Factorization](https://mlsanity.com/paper/2606.00584/) (2026)

## Abstract (arXiv)

An alternating-direction-method-based nonnegative latent factor model can perform efficient representation learning to a high-dimensional and incomplete (HDI) matrix. However, it introduces multiple hyper-parameters into the learning process, which should be chosen with care to enable its superior performance. Its hyper-parameter adaptation is desired for further enhancing its scalability. Targeting at this issue, this paper proposes an Adaptive Alternating-direction-method-based Nonnegative Latent Factor (A2NLF) model, whose hyper-parameter adaptation is implemented following the principle of particle swarm optimization. Empirical studies on nonnegative HDI matrices generated by industrial applications indicate that A2NLF outperforms several state-of-the-art models in terms of computational and storage efficiency, as well as maintains highly competitive estimation accuracy for an HDI matrix's missing data.