关注和学习:通过硬实例感知增强深度多视图集群

IF 15.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wenlong Liu , Jiaohua Qin
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引用次数: 0

摘要

深度对比多视图聚类旨在利用对比机制从多个特征中挖掘互补信息,近年来受到越来越多的关注。然而,我们观察到大多数对比多视图聚类方法在构建对比样本对的过程中忽略了硬样本造成的假样本对,包括负样本具有高相似度,而正样本具有低相似度。为了解决这个问题,我们提出了一种新的用于硬样本挖掘的深度对比多视图聚类网络,称为MVC-HSM。具体来说,我们提出了一种结合粗粒度透视图和细粒度透视图的策略。在粗粒度级别上,我们通过利用来自每个视图的原型来执行对比学习,从而减少样本级别上的硬样本。在细粒度层面,我们首先构建了一个基于表示关系和结构的综合评价函数来度量样本的相似性。结合高置信度伪标签的过滤效果,我们进一步设计了硬样本的对比学习损失。因此,该模型可以自动增加硬样本的权重,同时减少易样本的权重。在公共多视图数据集上进行了大量实验,验证了MVC-HSM的优越性,证明了该算法优于其他最先进的多视图聚类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Focus and learn: boosting deep multi-view clustering via hard instance awareness
Deep contrastive multi-view clustering aims to use contrastive mechanisms to exploit the complementary information from multiple features, which has attracted increasing attention in recent years. However, we observe that most contrastive multi-view clustering methods neglect the false sample pairs caused by hard samples during the process of constructing contrastive sample pairs, including negative samples exhibit high similarity and positive samples exhibit low similarity. To address this problems, we propose a novel deep contrastive multi-view clustering network for hard sample mining, termed MVC-HSM. Specifically, we propose a strategy that incorporates both coarse-grained and fine-grained perspectives. At the coarse-grained level, we perform contrastive learning by utilizing prototypes from each view, thereby mitigating hard samples at the sample level. At the fine-grained level, we first construct a comprehensive evaluation function to measure the similarity for the samples based on representation relationships and structures. In combination with the filtering effect of high-confidence pseudo-labels, we further design a contrastive learning loss for hard samples. Thus, the model could automatically increase the weight of hard samples while reducing the weight of easy samples. The superior of MVC-HSM is verified by extensive experiments on public multi-view datasets, demonstrating the proposed MVC-HSM outperforms other state-of-the-art multi-view clustering.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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