# An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis

arXiv: https://arxiv.org/abs/2401.07012 · HTML: https://mlsanity.com/paper/2401.07012/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 91 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022)
- [Accelerating Federated Learning via Momentum Gradient Descent](https://mlsanity.com/paper/1910.03197/) (2019)
- [A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model](https://mlsanity.com/paper/2208.02513/) (2022)
- [Matrix Computations and Optimization in Apache Spark](https://mlsanity.com/paper/1509.02256/) (2015)
- [Parallel Multi-Extended State Observers based {ADRC} with Application to High-Speed Precision Motion Stage](https://mlsanity.com/paper/2301.07269/) (2023)

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

- [Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2408.02704/) (2024)
- [Water Quality Data Imputation via A Fast Latent Factorization of Tensors with PID-based Optimizer](https://mlsanity.com/paper/2503.06997/) (2025)
- [A Proportional-Integral Controller-Incorporated SGD Algorithm for High Efficient Latent Factor Analysis](https://mlsanity.com/paper/2508.17609/) (2025)

## Abstract (arXiv)

High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix.