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.

翻译：多视角聚类因具有多源信息整合能力而受到广泛关注。尽管过去数十年涌现出众多先进方法，但大多数方法普遍忽视了弱监督信息的重要性，且未能保留多视角的特征属性，导致聚类性能不尽如人意。针对这些问题，本文提出一种新型深度多视角半监督聚类方法（DMSC），该方法在网络微调阶段联合优化三类损失函数，包括多视角聚类损失、半监督成对约束损失以及多自编码器重建损失。具体而言，基于KL散度的多视角聚类损失被施加于多视角数据的公共表示上，同时实现异质特征优化、多视角加权及聚类预测。随后，我们创新性地提出将成对约束集成到多视角聚类过程中，通过强制必须连接样本（不能连接样本）的所学多视角表示相似（不相似），使形成的聚类架构更具可信度。此外，不同于现有方法仅在网络微调时为每个异质分支保留编码器，我们进一步提出调整包含编码器和解码器的完整自编码器框架。通过这种方式，可缓解视角特定与视角共享特征空间严重受损的问题，使整个训练过程更加稳定。通过在八个主流图像数据集上的全面实验，我们证明所提方法性能优于当前最先进的多视角与单视角对比方法。