Tensor-based multi-view spectral clustering algorithms use tensors to model the structure of multi-dimensional data to take advantage of the complementary information and high-order correlations embedded in the graph, thus achieving impressive clustering performance. However, these algorithms use linear models to obtain consensus, which prevents the learned consensus from adequately representing the nonlinear structure of complex data. In order to address this issue, we propose a method called Generalized Latent Multi-View Clustering with Tensorized Bipartite Graph (GLMC-TBG). Specifically, in this paper we introduce neural networks to learn highly nonlinear mappings that encode nonlinear structures in graphs into latent representations. In addition, multiple views share the same latent consensus through nonlinear interactions. In this way, a more comprehensive common representation from multiple views can be achieved. An Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) framework is designed to optimize the model. Experiments on seven real-world data sets verify that the proposed algorithm is superior to state-of-the-art algorithms.

中文翻译：

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具有张量二部图的广义潜在多视图聚类

基于张量的多视图谱聚类算法使用张量对多维数据的结构进行建模，以利用图中嵌入的互补信息和高阶相关性，从而实现令人印象深刻的聚类性能。然而，这些算法使用线性模型来获得共识，这使得学习到的共识无法充分表示复杂数据的非线性结构。为了解决这个问题，我们提出了一种称为张量二分图的广义潜在多视图聚类（GLMC-TBG）的方法。具体来说，在本文中，我们引入神经网络来学习高度非线性映射，将图中的非线性结构编码为潜在表示。此外，多种观点通过非线性相互作用共享相同的潜在共识。通过这种方式，可以实现来自多个视图的更全面的共同表示。具有交替方向最小化的增强拉格朗日乘子 (ALM-ADM) 框架旨在优化模型。对七个真实世界数据集的实验验证了所提出的算法优于最先进的算法。