A novel vertical-cross-horizontal network

In order to integrate the ability of feature extraction of deep structure and short training time of broad structure, we propose a novel Vertical-Cross-Horizontal Network (VCHN) for data recognition, which mainly contains vertical operation, horizontal operation, nonlinear mapping and recognition decision. For vertical operation, we design a hierarchical structure, which is responsible for providing structural conditions for features evolution that are significant for classification decision. For horizontal operation, we use the fuzzy system with interpretability to design an expandable group of fuzzy subsystems to extract diverse features as much as possible, trying to replace the high-level features extracted via cascading more hidden layers. In that way, it mitigates the time-consuming burden generated by vertically deepening network blindly. The nonlinear mapping is used to transform extracted features into nonlinear ones, which are utilized to calculate the outputs for recognition decision. Extensive experiments show that the recognition accuracy of proposed method are 99.37% and 98.47% on ORL and EYaleB datasets, respectively. The proposed VCHN can not only mine the discriminative features via vertical operation, but also shorten the training time via the horizontal operation, which outperforms the other methods.