Graph Embedding Exploiting Subclasses

Recently, subspace learning methods for Dimensionality Reduction (DR), like Subclass Discriminant Analysis (SDA) and Clustering-based Discriminant Analysis (CDA), which use subclass information for the discrimination between the data classes, have attracted much attention. In parallel, important work has been accomplished on Graph Embedding (GE), which is a general framework unifying several subspace learning techniques. In this paper, GE has been extended in order to integrate subclass discriminant information resulting to the novel Subclass Graph Embedding (SGE) framework. The kernelization of SGE is also presented. It is shown that SGE comprises a generalization of the typical GE including subclass DR methods. In this context, the theoretical link of SDA and CDA methods with SGE is established. The efficacy and power of SGE has been substantiated by comparing subclass DR methods versus a diversity of unimodal methods all pertaining to the SGE framework via a series of experiments on various real-world data.