Robust sparse low-rank embedding for image dimension reduction

Abstract

Many methods based on matrix factorization have recently been proposed and achieve good performance in many practical applications. Latent low-rank representation (LatLRR) is a marvelous feature extraction method, and it has shown a powerful ability in extracting robust data features. However, LatLRR and the variants of LRR have some shortcomings as follows: (1) The label information of the original data are not considered, and they are usually unsupervised learning methods. (2) The local structure information is not preserved in the projected space. (3) The dimension of projection space is not reduced, and the extracted features do not have good and distinct interpretability. In order to solve the above problems, a new dimensionality reduction method based on low-rank representation termed robust sparse low-rank embedding (RSLRE) is proposed. Especially, by introducing the L2,1 norm constraint into the projected matrix, RSLRE algorithm can adaptively select the most discriminative and robust data features. In addition, two different matrices are introduced to ensure that projected feature dimensions can be reduced, and the obtained features can simultaneously maintain most of the energy of the observed samples. A large number of experiments on five public image datasets show that the proposed method can achieve very encouraging results compared with some classical feature extraction methods.