论文标题
深层嵌入式多视图聚类和协作培训
Deep Embedded Multi-view Clustering with Collaborative Training
论文作者
论文摘要
多视图聚类最近通过利用来自多个视图的信息引起了人们的注意。但是，现有的多视图聚类方法要么具有很高的计算和空间复杂性，要么缺乏表示能力。为了解决这些问题，我们在本文中提出了通过协作培训（DEMVC）深入嵌入的多视图聚类。首先，深层自动编码器单独学习了多个视图的嵌入式表示。然后，考虑了多种观点的共识和补充，并提出了一种新颖的协作培训计划。具体而言，所有视图的功能表示和集群分配都是协作学习的。进一步开发了一种新的一致性策略，用于群集中心的初始化，以通过协作培训来提高多视图聚类性能。几个流行的多视图数据集的实验结果表明，DEMVC比最先进的方法取得了重大改进。
Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with high computation and space complexities, or lack of representation capability. To address these issues, we propose deep embedded multi-view clustering with collaborative training (DEMVC) in this paper. Firstly, the embedded representations of multiple views are learned individually by deep autoencoders. Then, both consensus and complementary of multiple views are taken into account and a novel collaborative training scheme is proposed. Concretely, the feature representations and cluster assignments of all views are learned collaboratively. A new consistency strategy for cluster centers initialization is further developed to improve the multi-view clustering performance with collaborative training. Experimental results on several popular multi-view datasets show that DEMVC achieves significant improvements over state-of-the-art methods.
