Zhang Chen, Wang Yingxu, Wang Xuesong, Chen C L Philip, Chen Long, Chen Yuehui, Du Tao, Yang Cheng, Liu Bowen, Zhou Jin
IEEE Trans Neural Netw Learn Syst. 2024 Sep 4;PP. doi: 10.1109/TNNLS.2024.3447006.
Deep multiview clustering provides an efficient way to analyze the data consisting of multiple modalities and features. Recently, the autoencoder (AE)-based deep multiview clustering algorithms have attracted intensive attention by virtue of their rewarding capabilities of extracting inherent features. Nevertheless, most existing methods are still confronted by several problems. First, the multiview data usually contains abundant cross-view information, thus parallel performing an individual AE for each view and directly combining the extracted latent together can hardly construct an informative view-consensus feature space for clustering. Second, the intrinsic local structures of multiview data are complicated, hence simply embedding a preset graph constraint into multiview clustering models cannot guarantee expected performance. Third, current methods commonly utilize the Kullback-Leibler (KL) divergence as clustering loss and accordingly may yield appalling clusters that lack discriminate characters. To solve these issues, in this article we propose two new AE-based deep multiview clustering algorithms named AE-based deep multiview clustering model incorporating graph embedding (AG-DMC) and deep discriminative multiview clustering algorithm with adaptive graph constraint (ADG-DMC). In AG-DMC, a novel cross-view representation learning model is established delicately by performing decoding processes based on the cascaded view-specific latent to learn sound view-consensus features for inspiring clustering results. In addition, an entropy-regularized adaptive graph constraint is imposed on the obtained soft assignments of data to precisely preserve potential local structures. Furthermore, in the improved model ADG-DMC, the adversarial learning mechanism is adopted as clustering loss to strengthen the discrimination of different clusters for better performance. In the comprehensive experiments carried out on eight real-world datasets, the proposed algorithms have achieved superior performance in the comparison with other advanced multiview clustering algorithms.
深度多视图聚类为分析由多种模态和特征组成的数据提供了一种有效方法。近年来，基于自动编码器（AE）的深度多视图聚类算法凭借其提取固有特征的出色能力而备受关注。然而，大多数现有方法仍面临若干问题。首先，多视图数据通常包含丰富的跨视图信息，因此为每个视图并行执行单个自动编码器并直接将提取的潜在特征组合在一起，很难构建一个用于聚类的信息丰富的视图一致特征空间。其次，多视图数据的内在局部结构复杂，因此简单地将预设的图约束嵌入到多视图聚类模型中不能保证预期性能。第三，当前方法通常使用库尔贝克-莱布勒（KL）散度作为聚类损失，因此可能会产生缺乏区分性的糟糕聚类。为了解决这些问题，在本文中我们提出了两种新的基于自动编码器的深度多视图聚类算法，即结合图嵌入的基于自动编码器的深度多视图聚类模型（AG-DMC）和具有自适应图约束的深度判别多视图聚类算法（ADG-DMC）。在AG-DMC中，通过基于级联的特定视图潜在特征执行解码过程，精心建立了一个新颖的跨视图表示学习模型，以学习良好的视图一致特征来促进聚类结果。此外，对获得的数据软分配施加熵正则化自适应图约束，以精确保留潜在的局部结构。此外，在改进模型ADG-DMC中，采用对抗学习机制作为聚类损失，以增强不同聚类的区分性，从而获得更好的性能。在对八个真实世界数据集进行的综合实验中，所提出的算法与其他先进的多视图聚类算法相比取得了优异的性能。
