论文标题
多视图子空间集群的联合特征加权和LOBAL结构学习
Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering
论文作者
论文摘要
多视图聚类集成了多个功能集，这些功能集揭示了数据的不同方面并提供互补信息，以提高聚类性能。有效利用多个视图的互补信息仍然具有挑战性，因为原始数据通常包含噪声并且高度冗余。此外，大多数现有的多视图聚类方法仅旨在探索所有视图的一致性，同时忽略每个视图的本地结构。但是，有必要考虑每个视图的局部结构，因为不同的视图会呈现不同的几何结构，同时接受相同的群集结构。为了解决上述问题，我们通过同时为不同功能分配权重，并在特定于视图特定的自我代表特征空间中捕获数据的本地信息，从而提出了一种新颖的多视图子空间聚类方法。特别是，采用了共同的群集结构正则化来确保不同观点之间的一致性。还开发了基于增强拉格朗日乘数的有效算法来解决相关的优化问题。在几个基准数据集上进行的实验表明，所提出的方法可实现最先进的性能。我们在https://github.com/ekin102003/jflmsc上提供MATLAB代码。
Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other, to improve the clustering performance. It remains challenging to effectively exploit complementary information across multiple views since the original data often contain noise and are highly redundant. Moreover, most existing multi-view clustering methods only aim to explore the consistency of all views while ignoring the local structure of each view. However, it is necessary to take the local structure of each view into consideration, because different views would present different geometric structures while admitting the same cluster structure. To address the above issues, we propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces. Especially, a common cluster structure regularization is adopted to guarantee consistency among different views. An efficient algorithm based on an augmented Lagrangian multiplier is also developed to solve the associated optimization problem. Experiments conducted on several benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance. We provide the Matlab code on https://github.com/Ekin102003/JFLMSC.
