Dual Contrast-Driven Deep Multi-view Clustering

计算机科学 人工智能 聚类分析 对比度(视觉) 对偶(语法数字) 模式识别(心理学) 计算机视觉 文学类 艺术
作者
Jinrong Cui,Yuting Li,Han Huang,Jie Wen
出处
期刊:IEEE transactions on image processing [Institute of Electrical and Electronics Engineers]
卷期号:33: 4753-4764 被引量:5
标识
DOI:10.1109/tip.2024.3444269
摘要

Consensus representation learning is one of the most popular approaches in the field of multi-view clustering. However, most of the existing methods cannot learn discriminative representations with a clustering-friendly structure since these methods ignore the separation among clusters and the compactness within each cluster. To tackle this issue, we propose a new deep multi-view clustering network with a dual contrastive mechanism to learn clustering-friendly representations. Specifically, our method employs dual contrasting losses: a dynamic cluster diffusion loss to maximize the distance between different clusters and a reliable neighbor-guided positive alignment loss to enhance compactness within each cluster. Our approach includes several key components: view-specific encoders to extract high-level features from each view, and an adaptive feature fusion strategy to obtain consensus representations across multiple views. The dynamic cluster diffusion module ensures inter-cluster separation by maximizing distances between different clusters in the consensus feature space. Simultaneously, the reliable neighbor-guided positive alignment module improves within-cluster compactness through a pseudo-label and nearest neighbor structure-driven contrastive loss. Experimental results on several datasets show that our method can acquire clustering-friendly representations with both good properties of inter-cluster separation and within-cluster compactness, and outperforms the existing state-of-the-art approaches in clustering performance. Our source code is available at https://github.com/tweety1028/DCMVC.
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