Skip to Main Content
In this paper, we develop a new feature extraction and dimension reduction technique: 2-dimensional adaptive discriminant analysis (2DADA) based on 2DLDA and our proposed 2DBDA. It effectively exploits favorable attributes of both 2DBDA and 2DLDA and avoids their unfavorable ones. 2DADA can easily find an optimal discriminative subspace with adaptation to different sample distributions. It not only alleviates the problem of high dimensionality, but also enhances the classification performance in the subspace with KNN classifier. Experimental results on hand-written digit database and face databases show an improvement of 2DADA over other traditional dimension reduction techniques.