论文标题
丰富的强大多视图内核子空间群集
Enriched Robust Multi-View Kernel Subspace Clustering
论文作者
论文摘要
子空间聚类是找到基础低维子空间并正确群集数据点。在本文中，我们提出了一种新型的多视图子空间聚类方法。大多数现有方法都有两个关键问题。首先，他们通常采用两个阶段的框架，并隔离亲和力学习，多视图信息融合和聚类的过程。其次，他们认为数据在于线性子空间中可能在实践中失败，因为大多数现实世界数据集可能具有非线性结构。为了解决上述问题，在本文中，我们提出了一种新颖的富含鲁棒的多视图内核子空间聚类框架，其中从多视图数据和频谱聚类中都学到了共识亲和力矩阵。由于难以优化的目标和约束，我们提出了一种迭代优化方法，该方法易于实现，并且可以在每个步骤中产生封闭的解决方案。广泛的实验验证了我们方法比最新聚类方法的优越性。
Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.
