Supervised orthogonal discriminant projection based on double adjacency graphs for image classification

This study proposes a supervised orthogonal discriminant projection (SODP) based on double adjacency graphs (DAGs). SODP based on DAG (SODP-DAG) aims to minimise the local within-class scatter and simultaneously maximise both the local between-class scatter and the non-local scatter, where the local between-class scatter and the local within-class scatter are constructed by applying the DAG structure. By doing so, SODP-DAG can keep the local within-class structure for original data and find the optimal discriminant directions effectively. Moreover, four schemes are designed for constructing weight matrices in SODP-DAG. To validate the performance of SODP-DAG, the authors compared it with orthogonal discriminant projection, SODP and others on several publicly available datasets. Experimental results show the feasibility and effectiveness of SODP-DAG.