GLocal Structural Feature Selection with Sparsity for Multimedia Data Understanding

The selection of discriminative features is an important and effective technique for many multimedia tasks. Using ir- relevant features in classification or clustering tasks could deteriorate the performance. Thus, designing eficient fea- Ture selection algorithms to remove the irrelevant features is a possible way to improve the classification or clustering performance. With the successful usage of sparse models in image and video classification and understanding, imposing structural sparsity in feature selection has been widely in- vestigated during the past years. Motivated by the merit of sparse models, we propose a novel feature selection method using a sparse model in this paper. Different from the state of the art, our method is built upon ̀2;p-norm and simultane- ously considers both the global and local (GLocal) structures of data distribution. Our method is more exible in selecting the discriminating features as it is able to control the degree of sparseness. Moreover, consid...