Abstract: A new subspace learning algorithm called Two-dimensional Nonparametric Discriminant Analysis Algorithm Based on Nearest Feature Line (TDNDA-NFL) is proposed for pattern classification, such as face recognition. The proposed algorithm integrates the idea of NFL and two-dimensional nonparametric discriminant algorithm. It computes the nearest feature distance based on the idea of NFL in the subspace learning stage, then it computes the low-dimensional subspace using two-dimensional nonparametric discriminant algorithm. It classifies in the projected space. In experiments the proposed method is evaluated by the ORL databases and computed with several state-of-the-art algorithms. According to the computed results, the proposed method outperformes other algorithms.
Nearest Feature Line(NFL),
Two-dimensional Nonparametric Discriminant Analysis(TDNDA), subspace learning,