Feature Extraction Based on Class Mean Embedding (CME)

Recently, local discriminant embedding (LDE) was proposed to manifold learning and pattern classification. In LDE framework, the neighbor and class of data points were used to construct the graph embedding for classification problems. From a high dimensional to a low dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring data points of different classes no longer stick to one another. But, neighboring data points of different classes are not deemphasized efficiently by LDE and it may degrade the performance of classification. In this paper, we investigated its extension, called class mean embedding (CME), using class mean of data points to enhance its discriminant power in their mapping into a low dimensional space. Experimental results on ORL and FERET face databases show the effectiveness of the proposed method.