Unsupervised robust discriminative manifold embedding with self-expressiveness

Abstract

Dimensionality reduction has obtained increasing attention in the machine learning and computer vision communities due to the curse of dimensionality. Many manifold embedding methods have been proposed for dimensionality reduction. Many of them are supervised and based on graph regularization whose weight affinity is determined by original noiseless data. When data are noisy, their performance may degrade. To address this issue, we present a novel unsupervised robust discriminative manifold embedding approach called URDME, which aims to offer a joint framework of dimensionality reduction, discriminative subspace learning , robust affinity representation and discriminative manifold embedding. The learned robust affinity not only captures the global geometry and intrinsic structure of underlying high-dimensional data, but also satisfies the self-expressiveness property. In addition, the learned projection matrix owns discriminative ability in the low-dimensional subspace. Experimental results on several public benchmark datasets corroborate the effectiveness of our approach and show its competitive performance compared with the related methods.