Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 4; pp. 2956 - 2963
• Main Authors: Ren, Zhenwen, Mukherjee, Mithun, Lloret, Jaime, Venu, P.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-04-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; cognitive computing; Cognitive systems; Electronic mail; graph learning; Graph theory; Graphs; Industrial applications; industrial Internet of Things (IIoT); Informatics; Internet of Things; Kernel; Kernels; Machine learning; Manifolds; multiple kernel clustering (MKC); Venus
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3010357

In the cognitive computing of intelligent industrial Internet of Things, clustering is a fundamental machine learning problem to exploit the latent data relationships. To overcome the challenge of kernel choice for nonlinear clustering tasks, multiple kernel clustering (MKC) has attracted intensive attention. However, existing graph-based MKC methods mainly aim to learn a consensus kernel as well as an affinity graph from multiple candidate kernels, which cannot fully exploit the latent graph information. In this article, we propose a novel pure graph-based MKC method. Specifically, a new graph model is proposed to preserve the local manifold structure of the data in kernel space so as to learn multiple candidate graphs. Afterward, the latent consistency and selfishness of these candidate graphs are fully considered. Furthermore, a graph connectivity constraint is introduced to avoid requiring any postprocessing clustering step. Comprehensive experimental results demonstrate the superiority of our method.