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Multi-view Spectral Clustering via Tensor-SVD Decomposition

机译:通过Tensor-SVD分解的多视图光谱聚类

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Multi-view clustering has attracted considerable attention in recent years, some related approaches always use matrices to represent views, and model by capturing two dimensional structure among views. The critical deficiency of these work is ignoring the space structure information of all views, which results in the mediocre performance of clustering. In this paper, we propose a novel Tensor-SVD decomposition based Multi-view Spectral Clustering algorithm(TMSC) to iron out flaws. Our method firstly puts transition probability matrices of all views into a three-order tensor, which naturally reserves the whole structure information of data. Then it establishes a low multi-rank tensor model based on tensor-SVD decomposition by fully mining the complementary information among multiple views. Another difficulty in this paper is that the optimal objective of TMSC has a low multirank constraint on the transition probability tensor, and a probabilistic simplex constraint on each fiber of the tensor. To tackle this challenging problem, we design an optimization procedure based on the Augmented Lagrangian Multiplier scheme. Experimental results on real word datasets show that TMSC has superior clustering quality over several state-of-theart multi-view clustering approaches.
机译:近年来,多视图聚类吸引了相当多的关注,一些相关方法始终使用矩阵来表示视图,并通过捕获视图之间的二维结构进行建模。这些工作的关键缺陷是忽略了所有视图的空间结构信息,从而导致聚类的性能中等。在本文中,我们提出了一种基于Tensor-SVD分解的多视图光谱聚类算法(TMSC)来消除缺陷。我们的方法首先将所有视图的转移概率矩阵放入一个三阶张量,这自然保留了数据的整个结构信息。然后,通过充分挖掘多个视图之间的互补信息,基于张量-SVD分解建立低阶多张量张量模型。本文的另一个困难是,TMSC的最优目标对转移概率张量的多秩约束较低,并且在张量的每根光纤上均具有概率单纯形约束。为了解决这个具有挑战性的问题,我们设计了基于增强拉格朗日乘数方案的优化程序。在实词数据集上的实验结果表明,与几种最新的多视图聚类方法相比,TMC具有更好的聚类质量。

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