论文标题
多视图光谱聚类量身定制的张量低级别表示
Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation
论文作者
论文摘要
本文探讨了基于张量低级别建模的多视图光谱聚类（MVSC）的问题。与现有的方法不同，所有方法都采用了现成的张量低级别标准，而无需考虑MVSC中张量的特殊特性，我们设计了一种针对MVSC量身定制的新型结构化张量低级别标准。具体而言，我们明确对张量的额叶和水平切片施加了对称的低级别约束和结构化稀疏的低级约束，以分别表征视图和视图之间的关系。此外，可以共同优化这两个约束以实现相互优化。根据新型张量低级别的规范，我们将MVSC作为凸低量张量恢复问题制定，然后通过基于增强的Lagrange乘数方法有效地解决该问题。五个基准数据集的广泛实验结果表明，所提出的方法在很大程度上优于最先进的方法。令人印象深刻的是，我们的方法能够产生完美的聚类。此外，我们方法的参数可以很容易地调整，并且所提出的模型对不同的数据集具有鲁棒性，从而证明了其实践的潜力。
This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. Unlike the existing methods that all adopt an off-the-shelf tensor low-rank norm without considering the special characteristics of the tensor in MVSC, we design a novel structured tensor low-rank norm tailored to MVSC. Specifically, we explicitly impose a symmetric low-rank constraint and a structured sparse low-rank constraint on the frontal and horizontal slices of the tensor to characterize the intra-view and inter-view relationships, respectively. Moreover, the two constraints could be jointly optimized to achieve mutual refinement. On the basis of the novel tensor low-rank norm, we formulate MVSC as a convex low-rank tensor recovery problem, which is then efficiently solved with an augmented Lagrange multiplier based method iteratively. Extensive experimental results on five benchmark datasets show that the proposed method outperforms state-of-the-art methods to a significant extent. Impressively, our method is able to produce perfect clustering. In addition, the parameters of our method can be easily tuned, and the proposed model is robust to different datasets, demonstrating its potential in practice.
