Incremental Deep Computation Model for Wireless Big Data Feature Learning

Big data feature learning is a crucial issue for the service management for Internet of Things. However, big data collected from Internet of Things is of dynamic nature at a high speed, which poses an important challenge on wireless big data learning models, especially the deep computation model. In this paper, an incremental deep computation model is proposed for wireless big data feature learning in Internet of Things. First, two incremental tensor auto-encoders (ITAE) are developed by devising two incremental learning algorithms, namely parameter-based incremental learning algorithm (PI-TAE) and structure-based incremental learning algorithm (SI-TAE), when new wireless samples are available. PI-TAE only updates the network parameters while SI-TAE simultaneously adjusts the structure and updates the parameters to adapt to the new arriving wireless big data. Furthermore, an incremental deep computation model is constructed by stacking several ITAEs. Experiments are conducted to evaluate the performance of the proposed model by comparing with the conventional deep computation model and other two representative incremental learning algorithms, i.e., OANN and PIE. Results demonstrate that the presented model can modify the network in an incremental manner for new arriving data learning efficiently with preserving the prior knowledge for the previous data learning, proving its potential for dynamic wireless big data learning in Internet of Things.