Multichannel Matrix Randomized Autoencoder

Abstract

The existing randomized autoencoders are generally designed for vectorization data resulting in destroying the original structure information inevitably when dealing with multi-dimension data such as image and video. To address this issue, a one-side matrix randomized AE (OMRAE) is developed that takes the two-dimensional (2D) data as inputs directly by the linear mapping on one-side of inputs with matrix multiplication. For multichannel 2D (M2D) data, a multichannel OMRAE (OMMRAE) is proposed by training the output weights to rebuild each channel of inputs respectively. In this way, the structural information of each channel and the interaction between channels are explored. Then, a double-side structure using 2 OMMRAEs to simultaneously extracts the row and column structure information of M2D is developed. At last, a novel hierarchical matrix randomized neural networks is constructed for one-class classification where each layer passes information by bilinear mapping derived from DMMRAE. Experiments are conducted on 2 benchmark datasets for the effectiveness demonstration. Comparisons to several state-of-the-art AEs reveal that the proposed OMMRAE/DMMRAE can obtain better performance with a compact network size. The source code would be available at https://github.com/ML-HDU/MMRAE .