Ye Xulun, Zhao Jieyu, Chen Yu
Institute of Computer Science and Technology, Ningbo University, Ningbo 315211, China.
Entropy (Basel). 2018 Oct 29;20(11):830. doi: 10.3390/e20110830.
Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge. In this paper, a novel unsupervised generative clustering approach within the Bayesian nonparametric framework has been proposed. Specifically, our manifold method automatically selects the cluster number with a Dirichlet Process (DP) prior. Then, a DP-based mixture model with constrained Mixture of Gaussians (MoG) is constructed to handle the manifold data. Finally, we integrate our model with the -nearest neighbor graph to capture the manifold geometric information. An efficient optimization algorithm has also been derived to do the model inference and optimization. Experimental results on synthetic datasets and real-world benchmark datasets exhibit the effectiveness of this new DP-based manifold method.
多流形聚类是信号处理和机器学习中最基本的任务之一。尽管现有的多流形聚类方法非常强大，但从数据中自动学习聚类数量仍然是一个挑战。本文提出了一种在贝叶斯非参数框架内的新型无监督生成聚类方法。具体而言，我们的流形方法使用狄利克雷过程（DP）先验自动选择聚类数量。然后，构建一个基于DP的混合模型，该模型具有受限的高斯混合（MoG）来处理流形数据。最后，我们将模型与最近邻图集成以捕获流形几何信息。还推导了一种有效的优化算法来进行模型推理和优化。在合成数据集和真实世界基准数据集上的实验结果表明了这种基于DP的新流形方法的有效性。
