通过重新对齐实现部分多视角聚类

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2024-11-12 DOI:10.1016/j.neunet.2024.106884
Wenbiao Yan , Jihua Zhu , Jinqian Chen , Haozhe Cheng , Shunshun Bai , Liang Duan , Qinghai Zheng
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引用次数: 0

摘要

多视图聚类从多视图数据中学习一致的信息,旨在获得更显著的聚类特征。然而,现实世界中的数据往往表现出时间或空间上的不同步,导致视图中的实例不对齐。现有方法主要通过学习变换矩阵来对齐不对齐的实例,但这种学习可微分变换矩阵的过程非常繁琐。为了应对部分未对齐实例的挑战,我们提出了通过重新对齐进行部分多视图聚类(PMVCR)的方法。我们的方法通过两阶段的训练和重新对齐过程,将表示学习和数据对齐整合在一起。具体来说,我们的训练过程包括三个阶段:(i) 在粗粒度对齐阶段,我们为未对齐的实例构建负实例对,并利用对比学习初步学习实例的视图表示。(ii) 在重新对齐阶段,我们根据视图表示的相似性匹配未对齐的实例,使其与主视图对齐。(iii) 在细粒度对齐阶段,我们进一步增强了视图表征的判别能力和模型区分聚类的能力。与现有模型相比,我们的方法有效地利用了未对齐样本之间的信息,并通过构建负实例对增强了模型的泛化能力。在几个流行的多视图数据集上进行的聚类实验证明了我们方法的有效性和优越性。我们的代码可在 https://github.com/WenB777/PMVCR.git 公开获取。
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Partially multi-view clustering via re-alignment
Multi-view clustering learns consistent information from multi-view data, aiming to achieve more significant clustering characteristics. However, data in real-world scenarios often exhibit temporal or spatial asynchrony, leading to views with unaligned instances. Existing methods primarily address this issue by learning transformation matrices to align unaligned instances, but this process of learning differentiable transformation matrices is cumbersome. To address the challenge of partially unaligned instances, we propose Partially Multi-view Clustering via Re-alignment (PMVCR). Our approach integrates representation learning and data alignment through a two-stage training and a re-alignment process. Specifically, our training process consists of three stages: (i) In the coarse-grained alignment stage, we construct negative instance pairs for unaligned instances and utilize contrastive learning to preliminarily learn the view representations of the instances. (ii) In the re-alignment stage, we match unaligned instances based on the similarity of their view representations, aligning them with the primary view. (iii) In the fine-grained alignment stage, we further enhance the discriminative power of the view representations and the model’s ability to differentiate between clusters. Compared to existing models, our method effectively leverages information between unaligned samples and enhances model generalization by constructing negative instance pairs. Clustering experiments on several popular multi-view datasets demonstrate the effectiveness and superiority of our method. Our code is publicly available at https://github.com/WenB777/PMVCR.git.
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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