Correction to: Feature selection via uncorrelated discriminant sparse regression for multimedia analysis

As an important part of multimedia analysis applications, feature selection has attracted much attention during the past decades. Lots of feature selection methods have been proposed, but most of them neglect to consider the correlation between the selected features, which leads to the feature redundancy problem. In this paper, we propose a novel supervised feature selection method, termed as Uncorrelated Discriminant Sparse Regression (UDSR). This method is an organic combination of discriminant sparse regression and uncorrelated constraint. In this method, the discriminant sparse regression ensures the discriminant power of the selected features, and the uncorrelated constraint avoids the redundancy of selected features. Thus the features selected by our method are not only discriminative but also uncorrelated with each other. The method can be applied to a wide range of multimedia applications. Experiments are conducted on two video datasets and four image datasets. The experimental results show that the proposed method has better performance for multimedia analysis, compared to the baseline and six state-of-the-art relative methods.