Abstract
In this paper, a novel subspace learning algorithm, called neighborhood discriminant nearest feature line analysis (NDNFLA), is proposed. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) scatter and minimizing the within-class FL scatter. At the same time, the neighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 127-132 |
| Number of pages | 6 |
| Journal | Journal of Internet Technology |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2013 |
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Cite this
Yan, L., Zheng, W., Chu, S-C., & Roddick, J. (2013). Neighborhood Discriminant Nearest Feature Line Analysis and Its Application to Face Recognition. Journal of Internet Technology, 14(1), 127-132. https://doi.org/10.6138/JIT.2013.14.1.13