Concept Factorization via Self-Representation and Adaptive Graph Structure Learning

TL;DR

This paper introduces CFSRAG, a novel concept factorization model that adaptively learns data graph structures through self-representation, enhancing clustering performance by dynamically capturing the dataset's geometric structure.

Contribution

The paper proposes an adaptive graph learning approach within concept factorization using self-representation, improving clustering by dynamically modeling data relationships.

Findings

Outperforms state-of-the-art models on four datasets.

Effectively learns dynamic data affinity relationships.

Enhances clustering accuracy through adaptive graph regularization.

Abstract

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.