Robust and Flexible Graph-based Semi-supervised Embedding

This paper introduces a robust and flexible graph-based semi-supervised embedding method for generic classification and recognition tasks. It combines the merits of sparsity preserving projections, margin maximization, and robust loss function. The latter reduces the effect of outliers on the regression model needed for mapping unseen examples. Furthermore, unlike label propagation semi-supervised schemes, our proposed method is a data embedding into a space whose dimension is not limited to the number of classes. The used robust norm combines the merits of matrix l(1,2) and l(2) norms. It is suited for the Laplacian distribution of outliers and the Gaussian distribution of samples with small losses. We provide experiments on four benchmark image datasets in order to study the performance of the proposed method. These experiments show that the proposed methods can be more discriminative than other state-of-the-art methods.