Multi-Modal Joint Clustering With Application for Unsupervised Attribute Discovery
Multi-Modal Joint Clustering With Application for Unsupervised Attribute Discovery
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DOI：
10.1109/tip.2018.2831454
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发表时间：
2018-05
影响因子：
10.6
通讯作者：
Liangchen Liu;F. Nie;A. Wiliem;Zhihui Li;Teng Zhang;B. Lovell
中科院分区：
文献类型：
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作者：
Liangchen Liu;F. Nie;A. Wiliem;Zhihui Li;Teng Zhang;B. Lovell
利用对象的多个描述/视图在图像聚类任务中通常很有用。尽管已经提出了许多有效聚类多视图数据的工作，但仍然存在未解决的问题，例如由于两个不相交的阶段而导致传统的基于谱的聚类方法引入的错误：1）特征分解和2）新表示的离散化。在本文中，我们提出了一个统一的聚类框架，该框架共同学习这两个阶段并利用数据的多种描述。更具体地说，该框架提出了两种学习方法：1）通过从不同视图构建图，2）通过组合多个图。此外，受益于所提出方法的可分离性和局部图保留特性，提出了一种新颖的无监督自动属性发现方法。我们在五个数据集上验证了我们的方法的有效性，表明所提出的联合学习聚类方法优于最近最先进的方法。我们还表明，可以导出一种新颖的方法来解决无监督的自动属性发现任务。
Utilizing multiple descriptions/views of an object is often useful in image clustering tasks. Despite many works that have been proposed to effectively cluster multi-view data, there are still unaddressed problems such as the errors introduced by the traditional spectral-based clustering methods due to the two disjoint stages: 1) eigendecomposition and 2) the discretization of new representations. In this paper, we propose a unified clustering framework which jointly learns the two stages together as well as utilizing multiple descriptions of the data. More specifically, two learning methods from this framework are proposed: 1) through a graph construction from different views and 2) through combining multiple graphs. Furthermore, benefiting from the separability and local graph preserving properties of the proposed methods, a novel unsupervised automatic attribute discovery method is proposed. We validate the efficacy of our methods on five data sets, showing that the proposed joint learning clustering methods outperform the recent state-of-the-art methods. We also show that it is possible to derive a novel method to address the unsupervised automatic attribute discovery tasks.