Abstract
A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.
Original language | English |
---|---|
Pages | 344-347 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2011 |
Event | 2nd International Conference on Innovations in Bio-inspired Computing and Applications - Duration: 16 Dec 2011 → … |
Conference
Conference | 2nd International Conference on Innovations in Bio-inspired Computing and Applications |
---|---|
Period | 16/12/11 → … |
Keywords
- face
- Nearest feature line
- subspace learning