Subspace embedding for classification.

Subspace embedding is a popular technique to discover a mapping space in which the samples are expected to be represented appropriately. In recent years, graph has received increasing attention in subspace embedding and most of these related graph-based algorithms directly construct the connecting graph in original space. But some redundant information probably exists in the data with high dimension, and thus, it is hard to ensure the quality of graph. In this paper, we propose a novel discriminative subspace embedding (DSE) algorithm for classification. DSE is a supervised subspace learning method. In DSE, an intra-class graph and an inter-class graph are used to characterize the relationship among samples from the same class and different classes, respectively. DSE assumes that the embeddings of samples from the same class should be similar while different embeddings should be learned for the samples belonging to different classes. Based on this assumption, the above two graphs are constructed in mapping space. In order to enhance the quality of projections, the reconstruction of original data is also taken into consideration in DSE. Finally, some datasets are adopted to test the performance of DSE. Experimental results illustrate that effective representations can be learned by DSE and it has a more competitive learning ability, in comparison with related algorithms. [ABSTRACT FROM AUTHOR]