Adaptive maximum margin criterion for image classification
We propose in this paper a novel adaptive maximum margin criterion (AMMC) method for image classification. While a large number of discriminant analysis algorithms have been proposed in recent years, most of them consider an equal importance of each training sample and ignore the different contributions of these samples to learn the discriminative feature subspace for classification. Motivated by the fact that some training samples are more effectual in learning the low-dimensional feature space than other samples, we propose using different weights to characterize the different contributions of the training samples and incorporate such weighting information into the popular maximum margin criterion algorithm to devise the corresponding AMMC for image classification. Moreover, we extend the proposed MMC algorithm to the semi-supervised case, namely, semi-supervised adaptive maximum margin criterion (SAMMC), by making use of both labeled and unlabeled samples to further improve the classification performance. Experimental results are presented to demonstrate the efficacy of the proposed methods.