A canonical polyadic deep convolutional computation model for big data feature learning in Internet of Things

Abstract

Abstract In recent years, the Internet of Things is more widely deployed with increasing amounts of data gathered. These data are of high volume, velocity, veracity and variety, posing a vast challenge on the data analysis, especially with respect to variety and velocity. To address this challenge, a canonical polyadic deep convolutional computation model is introduced to efficiently and effectively capture the hierarchical representation of the big data by employing the canonical polyadic decomposition to factorize the deep convolutional computation. In particular, to speed up the learning of local topologies hidden in the big data, a canonical polyadic convolutional kernel is devised by compacting the tensor convolutional kernel into the linear combination of the principle rank-1 tensors. Furthermore, the canonical polyadic tensor fully-connected weight is used to efficiently map the correlation in the fully-connected layer. After that, the canonical polyadic high-order back-propagation is devised to train the canonical polyadic deep convolutional computation model. At last, detailed experiments are carried out on two well-known datasets. And results illustrate that the introduced model achieves higher performance than a competing model.