Abstract: | Marginal Fisher analysis (MFA) is a representative margin-based learning algorithm for face recognition. A major problem in
MFA is how to select appropriate parameters, k
1 and k
2, to construct the respective intrinsic and penalty graphs. In this paper, we propose a novel method called nearest-neighbor
(NN) classifier motivated marginal discriminant projections (NN-MDP). Motivated by the NN classifier, NN-MDP seeks a few projection
vectors to prevent data samples from being wrongly categorized. Like MFA, NN-MDP can characterize the compactness and separability
of samples simultaneously. Moreover, in contrast to MFA, NN-MDP can actively construct the intrinsic graph and penalty graph
without unknown parameters. Experimental results on the ORL, Yale, and FERET face databases show that NN-MDP not only avoids
the intractability, and high expense of neighborhood parameter selection, but is also more applicable to face recognition
with NN classifier than other methods. |