In multi-view clustering, different views may have different confidence
levels when learning a consensus representation. Existing methods usually
address this by assigning distinctive weights to different views. However, due
to noisy nature of real-world applications, the confidence levels of samples in
the same view may also vary. Thus considering a unified weight for a view may
lead to suboptimal solutions. In this paper, we propose a novel localized
multi-view subspace clustering model that considers the confidence levels of
both views and samples. By assigning weight to each sample under each view
properly, we can obtain a robust consensus representation via fusing the
noiseless structures among views and samples. We further develop a regularizer
on weight parameters based on the convex conjugacy theory, and samples weights
are determined in an adaptive manner. An efficient iterative algorithm is
developed with a convergence guarantee. Experimental results on four benchmarks
demonstrate the correctness and effectiveness of the proposed model.

本文提出了一种新的局部多视图子空间聚类模型，该模型考虑了不同视图和样本的置信度，并通过适当地为每个视图下的每个样本分配权重，从而获得了鲁棒的一致表示，进一步在权重参数上开发了一个基于凸共轭理论的正则化器，并以自适应方式确定样本权重，使用具有收敛保证的高效迭代算法，通过四个基准上的实验结果证明了该模型的正确性和有效性。