The Artificial Intelligence Internet of Things (AIoT) is an emerging concept aiming to perceive, understand and connect the ‘intelligent things’ to make the intercommunication of various networks and systems more efficient. A key step in achieving this goal is to carry out high-precision data analysis at the edge and cloud level. Clustering and dimensionality reduction in AIoT can facilitate efficient data management, storage, computing, and transmission of various data-driven AIoT applications. For high-efficiency data clustering and dimensionality reduction, this paper develops a prior-dependent graph (PDG) construction method to model and discover the complex relations of data. With the proper utilization and incorporation of data priors, i.e., (a) element local sparsity; (b) pair-wise symmetry; (c) multi-instance manifold smoothness; and (d) matrix low-rankness, the obtained graph has the characteristics of local sparsity, symmetry, low-rank, and can well reveal the complex multi-instance proximity among data points. The developed PDG model is then applied for two typical data analysis tasks, i.e., unsupervised data clustering and dimensionality reduction. Experimental results on multiple benchmark databases verify that, compared with some existing graph learning models, the PDG model can achieve substantial performance, which can be deployed in edge computing modules to provide efficient solutions for massive data management and applications in AIoT.

人工智能物联网（Artificial Intelligence Internet of Things，AIoT）是一个新兴概念，旨在感知、理解和连接“智能事物”，使各种网络和系统的互通更加高效。实现这一目标的关键一步是在边缘和云端进行高精度的数据分析。AIoT中的聚类和降维可以促进各种数据驱动的AIoT应用的高效数据管理、存储、计算和传输。为了高效的数据聚类和降维，本文开发了一种先验依赖图（PDG）构造方法来建模和发现数据的复杂关系。适当利用和合并数据先验，即 (a) 元素局部稀疏性；(b) 成对对称；(c) 多实例流形平滑度；(d)矩阵低秩，得到的图具有局部稀疏、对称、低秩的特点，可以很好地揭示数据点之间复杂的多实例邻近性。然后将开发的 PDG 模型应用于两个典型的数据分析任务，即无监督数据聚类和降维。在多个基准数据库上的实验结果证明，与现有的一些图学习模型相比，PDG模型可以取得可观的性能，可以部署在边缘计算模块中，为AIoT中的海量数据管理和应用提供高效的解决方案。然后将开发的 PDG 模型应用于两个典型的数据分析任务，即无监督数据聚类和降维。在多个基准数据库上的实验结果证明，与现有的一些图学习模型相比，PDG模型可以取得可观的性能，可以部署在边缘计算模块中，为AIoT中的海量数据管理和应用提供高效的解决方案。然后将开发的 PDG 模型应用于两个典型的数据分析任务，即无监督数据聚类和降维。在多个基准数据库上的实验结果证明，与现有的一些图学习模型相比，PDG模型可以取得可观的性能，可以部署在边缘计算模块中，为AIoT中的海量数据管理和应用提供高效的解决方案。