Graphical model, diffusion equation network dynamics, and manifold learning are different subareas in machine learning and network science. In this paper, combining the ideas from these subareas, we propose and implement graphical model and diffusion equation based manifold learning techniques for sensor data processing. We show that the graphical model and diffusion equation combined manifold learning can be used to perform data processing for one, two, and three-dimensional sensors. Experiments show that this manifold learning approach can solve many sensor data processing problems including radio frequency signal processing, image processing (computer vision), and three-dimensional lighting detection and ranging (LIDAR) processing problems better than some traditional methods.
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