# Concept Factorization via Self-Representation and Adaptive Graph Structure Learning

arXiv: https://arxiv.org/abs/2505.03390 · HTML: https://mlsanity.com/paper/2505.03390/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 84 references that are in the map.
- First posted on arXiv 6 May 2025.

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

- [Entropy Minimizing Matrix Factorization](https://mlsanity.com/paper/2103.13487/) (2021)
- [An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition](https://mlsanity.com/paper/2306.10826/) (2023)
- [High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection](https://mlsanity.com/paper/2203.03876/) (2022)

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

- [Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving](https://mlsanity.com/paper/2606.04338/) (2026)
- [Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network](https://mlsanity.com/paper/2606.09880/) (2026)

## Abstract (arXiv)

Concept Factorization (CF) models have attracted widespread attention due to their excellent performance in data clustering. In recent years, many variant models based on CF have achieved great success in clustering by taking into account the internal geometric manifold structure of the dataset and using graph regularization techniques. However, their clustering performance depends greatly on the construction of the initial graph structure. In order to enable adaptive learning of the graph structure of the data, we propose a Concept Factorization Based on Self-Representation and Adaptive Graph Structure Learning (CFSRAG) Model. CFSRAG learns the affinity relationship between data through a self-representation method, and uses the learned affinity matrix to implement dynamic graph regularization constraints, thereby ensuring dynamic learning of the internal geometric structure of the data. Finally, we give the CFSRAG update rule and convergence analysis, and conduct comparative experiments on four real datasets. The results show that our model outperforms other state-of-the-art models.