In this paper, we propose a novel Joint framework for Deep Multi-view
Clustering (DMJC), where multiple deep embedded features, multi-view fusion
mechanism and clustering assignments can be learned simultaneously. Our key
idea is that the joint learning strategy can sufficiently exploit
clustering-friendly multi-view features and useful multi-view complementary
information to improve the clustering performance. How to realize the
multi-view fusion in such a joint framework is the primary challenge. To do so,
we design two ingenious variants of deep multi-view joint clustering models
under the proposed framework, where multi-view fusion is implemented by two
different schemes. The first model, called DMJC-S, performs multi-view fusion
in an implicit way via a novel multi-view soft assignment distribution. The
second model, termed DMJC-T, defines a novel multi-view auxiliary target
distribution to conduct the multi-view fusion explicitly. Both DMJC-S and
DMJC-T are optimized under a KL divergence like clustering objective.
Experiments on six challenging image datasets demonstrate the superiority of
both DMJC-S and DMJC-T over single/multi-view baselines and the
state-of-the-art multiview clustering methods, which proves the effectiveness
of the proposed DMJC framework. To our best knowledge, this is the first work
to model the multi-view clustering in a deep joint framework, which will
provide a meaningful thinking in unsupervised multi-view learning.

本文提出了一个新的深度多视图联合聚类框架 (DMJC)，结合多个深度特征，多视图融合机制和聚类分配可同时学习，以提高聚类性能。通过两种不同的方案实现多视图融合，优化 KL 散度聚类目标来进行 DMJC-S 和 DMJC-T 的优化。实验证明，DMJC-S 和 DMJC-T 均优于单 / 多视图基线和现有的多视图聚类方法。此为首次将多视图聚类建模为深度联合框架，对于无监督多视图学习具有有意义的借鉴。