Supervised orthogonal discriminant projection based on double adjacency graphs for image classification
Bangjun Wang, Li Zhang, Fanzhang Li · IET Image Processing · 2017
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.