Tensor Deep Learning Model for Heterogeneous Data Fusion in Internet of Things

With the rapid evolvement of the Internet and data acquisition technology as well as the continuous advancement of science and technology, the amount of data in many fields has reached the level of terabyte or petabyte and most data collection comes from the Internet of Things (IoT). The rapid advancement of IoT big data has provided valuable opportunities for the development of people in all areas of society. At the same time, it has also brought severe challenges to various types of current information processing systems. Effectively using the big data technology, discovering the hidden laws in big data, tapping the potential value of big data, and predicting the development trend of things to allocate resources more reasonably will promote the overall development of society. However, most of the IoT big data are presented as heterogeneous data, with high dimensions, different forms of expression, and a lot of redundant information. The current machine learning model works in vector space, which makes it impossible to gain big data features because vectors cannot simulate the highly nonlinear distribution of IoT big data. This paper presents a deep learning calculation model called tensor deep learning (TDL), which further improves big data feature learning and high-level feature fusion. It uses tensors to model the complexity of multisource heterogeneous data and extends the vector space data to the tensor space, when feature extraction in the tensor space is included. To fully understand the underlying data distribution, the tensor distance is adopted as the average square sum error term of the output layer reconstruction error. Based on the conventional back-propagation algorithm, this study proposes a high-order back-propagation algorithm to extend the data from the linear space to multiple linear space and train the parameters of the proposed model. Then, to evaluate its performance, the proposed TDL model is compared with the stacked auto encoder and the multimodal deep learning model. Furthermore, experiments are performed on two representative datasets, namely CUAVE and STL-10. Experimental results show that the proposed model not only excels in heterogeneous data fusion but also provides a higher recognition accuracy than the conventional deep learning model or the multimodal learning model for big data.