# An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factors

arXiv: https://arxiv.org/abs/2208.06603 · HTML: https://mlsanity.com/paper/2208.06603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BAS: Beetle Antennae Search Algorithm for Optimization Problems](https://mlsanity.com/paper/1710.10724/) (2017)
- [Beetle Swarm Optimization Algorithm:Theory and Application](https://mlsanity.com/paper/1808.00206/) (2018)

## Abstract (arXiv)

Extracting the latent information in high-dimensional and incomplete matrices is an important and challenging issue. The Latent Factor Analysis (LFA) model can well handle the high-dimensional matrices analysis. Recently, Particle Swarm Optimization (PSO)-incorporated LFA models have been proposed to tune the hyper-parameters adaptively with high efficiency. However, the incorporation of PSO causes the premature problem. To address this issue, we propose a sequential Adam-adjusting-antennae BAS (A2BAS) optimization algorithm, which refines the latent factors obtained by the PSO-incorporated LFA model. The A2BAS algorithm consists of two sub-algorithms. First, we design an improved BAS algorithm which adjusts beetles' antennae and step-size with Adam; Second, we implement the improved BAS algorithm to optimize all the row and column latent factors sequentially. With experimental results on two real high-dimensional matrices, we demonstrate that our algorithm can effectively solve the premature convergence issue.