Spectral clustering is a popular tool in many unsupervised computer vision and machine learning tasks. Recently, due to the encouraging performance of deep neural networks, many conventional spectral clustering methods have been extended to the deep framework. Although these deep spectral clustering methods are quite powerful and effective, learning the cluster number from data is still a challenge. In this article, we aim to tackle this problem by integrating the spectral clustering, generative adversarial network and low rank model within a unified Bayesian framework. First, we adapt the low rank method to the cluster number estimation problem. Then, an adversarial-learning-based deep clustering method is proposed and incorporated. When introducing the spectral clustering method into our model clustering procedure, a hidden space structure preservation term is proposed. Via a Bayesian framework, the structure preservation term is embedded into the generative process, which can then be used to deduce a spectral clustering in the optimization procedure. Finally, we derive a variational-inference-based method and embed it into the network optimization and learning procedure. Experiments on different datasets prove that our model has the cluster number estimation capability and show that our method can outperform many similar graph clustering methods.

中文翻译：

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具有未知聚类号的贝叶斯对抗光谱聚类。

在许多无监督的计算机视觉和机器学习任务中，光谱聚类是一种流行的工具。最近，由于深度神经网络的令人鼓舞的性能，许多常规的频谱聚类方法已扩展到深度框架。尽管这些深谱聚类方法非常强大和有效，但是从数据中学习聚类数仍然是一个挑战。在本文中，我们旨在通过在统一的贝叶斯框架内集成频谱聚类，生成对抗网络和低秩模型来解决此问题。首先，我们将低秩方法应用于聚类数估计问题。然后，提出并结合了一种基于对抗学习的深度聚类方法。在将光谱聚类方法引入我们的模型聚类过程时，提出了一种隐蔽的空间结构保存术语。通过贝叶斯框架，将结构保留项嵌入到生成过程中，然后可以将其用于推导优化过程中的光谱聚类。最后，我们推导了一种基于变分推理的方法，并将其嵌入到网络优化和学习过程中。在不同数据集上的实验证明了我们的模型具有聚类估计能力，并且表明我们的方法可以胜过许多相似的图聚类方法。我们推导了一种基于变分推理的方法，并将其嵌入到网络优化和学习过程中。在不同数据集上的实验证明了我们的模型具有聚类估计能力，并且表明我们的方法可以胜过许多相似的图聚类方法。我们推导了一种基于变分推理的方法，并将其嵌入到网络优化和学习过程中。在不同数据集上的实验证明了我们的模型具有聚类估计能力，并且表明我们的方法可以胜过许多相似的图聚类方法。