论文标题
深度多视图半监督聚类,并带有样品成对约束
Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints
论文作者
论文摘要
由于多源信息集成的能力,多视图聚类吸引了很多关注。尽管在过去几十年中提出了许多高级方法,但其中大多数通常忽略了弱监督信息的重要性,并且无法保留多种视图的特征属性,从而导致聚类性能不令人满意。为了解决这些问题,在本文中,我们提出了一种新颖的深度观看半监督聚类(DMSC)方法,该方法共同优化了网络填充期间的三种损失,包括多视图集群损失,半监测的成对约束损失和多个自动范围损失和多个自动驱动器进行结构损失。具体而言,基于KL差异的多视图聚类损失被施加在多视图数据的共同表示上,以同时执行异质特征优化,多视图加权和聚类预测。然后,我们通过创新提议将成对约束集成到多视图聚类的过程中,通过执行所学到的必须链接样本(不能链接样本)的多视图表示形式相似(不同),从而使形成的群集体系结构可以更可靠。此外,与现有的竞争对手不同,该竞争对手仅保留网络填充期间每个异质分支的编码器,我们进一步建议调整包含编码器和解码器的完整自动编码器框架。这样,可以减轻特定视图和视图共享特征空间的严重腐败问题,从而使整个培训程序更加稳定。通过对八个流行图像数据集的全面实验,我们证明我们提出的方法的性能要比最先进的多视图和单视竞争者更好。
Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally overlook the significance of weakly-supervised information and fail to preserve the feature properties of multiple views, thus resulting in unsatisfactory clustering performance. To address these issues, in this paper, we propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method, which jointly optimizes three kinds of losses during networks finetuning, including multi-view clustering loss, semi-supervised pairwise constraint loss and multiple autoencoders reconstruction loss. Specifically, a KL divergence based multi-view clustering loss is imposed on the common representation of multi-view data to perform heterogeneous feature optimization, multi-view weighting and clustering prediction simultaneously. Then, we innovatively propose to integrate pairwise constraints into the process of multi-view clustering by enforcing the learned multi-view representation of must-link samples (cannot-link samples) to be similar (dissimilar), such that the formed clustering architecture can be more credible. Moreover, unlike existing rivals that only preserve the encoders for each heterogeneous branch during networks finetuning, we further propose to tune the intact autoencoders frame that contains both encoders and decoders. In this way, the issue of serious corruption of view-specific and view-shared feature space could be alleviated, making the whole training procedure more stable. Through comprehensive experiments on eight popular image datasets, we demonstrate that our proposed approach performs better than the state-of-the-art multi-view and single-view competitors.