College of Computer, National University of Defense Technology, Changsha, China.
Dongguan University of Technology, Dongguan, China.
Comput Intell Neurosci. 2018 Oct 1;2018:6148456. doi: 10.1155/2018/6148456. eCollection 2018.
In real-world applications of multiview clustering, some views may be incomplete due to noise, sensor failure, etc. Most existing studies in the field of incomplete multiview clustering have focused on early fusion strategies, for example, learning subspace from multiple views. However, these studies overlook the fact that clustering results with the visible instances in each view could be reliable under the random missing assumption; accordingly, it seems that learning a final clustering decision via late fusion of the clustering results from incomplete views would be more natural. To this end, we propose a late fusion method for incomplete multiview clustering. More specifically, the proposed method performs kernel -means clustering on the visible instances in each view and then performs a late fusion of the clustering results from different views. In the late fusion step of the proposed method, we encode each view's clustering result as a zero-one matrix, of which each row serves as a compressed representation of the corresponding instance. We then design an alternate updating algorithm to learn a unified clustering decision that can best group the visible compressed representations in each view according to the -means clustering objective. We compare the proposed method with several commonly used imputation methods and a representative early fusion method on six benchmark datasets. The superior clustering performance observed validates the effectiveness of the proposed method.
在多视图聚类的实际应用中，由于噪声、传感器故障等原因，某些视图可能是不完整的。在不完整多视图聚类领域的大多数现有研究中，都集中在早期融合策略上，例如，从多个视图学习子空间。然而，这些研究忽略了这样一个事实，即在随机缺失假设下，每个视图中可见实例的聚类结果可能是可靠的；因此，通过对不完整视图的聚类结果进行后期融合来学习最终的聚类决策似乎更为自然。为此，我们提出了一种不完整多视图聚类的后期融合方法。更具体地说，该方法对每个视图中的可见实例执行核均值聚类，然后对来自不同视图的聚类结果进行后期融合。在该方法的后期融合步骤中，我们将每个视图的聚类结果编码为零一矩阵，其中每一行作为对应实例的压缩表示。然后，我们设计了一种交替更新算法，根据 -means 聚类目标学习一个能够根据每个视图中可见的压缩表示对其进行最佳分组的统一聚类决策。我们将所提出的方法与六个基准数据集上的几种常用插补方法和一种代表性的早期融合方法进行了比较。观察到的优越聚类性能验证了该方法的有效性。
