A new graph-based clustering method with dual-feature regularization and Laplacian rank constraint

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hengdong Zhu , Yingshan Shen , Choujun Zhan , Fu Lee Wang , Heng Weng , Tianyong Hao
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引用次数: 0

Abstract

The performance of graph-based clustering is commonly limited by two-stage processing (Constructing and dividing similarity graph) and the quality of similar graphs. To this end, we propose a new graph-based clustering method with dual-feature regularization and Laplacian rank constraint. Specifically, our method reveals the clustering structure and unifies the two-stage process. It imposes a Laplacian rank constraint on the similarity graph to ensure that it has C connected components. In addition, a method based on dual-feature regularization is designed to capture local data feature information from both feature extraction and adaptive regression, and is applied to an accurate distance metric learning. A reweighting optimization is integrated to learn a high-quality robust similarity graph. Comprehensive experiments on Ecoli, Yale and Yeast datasets show that our method outperforms the existing graph-based clustering methods with an average improvement of about 4%, 5% and 7% on the evaluation metrics ACC, NMI and RI, respectively.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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