Multiview Hessian Semisupervised Sparse Feature Selection for Multimedia Analysis

Facing a large number of unlabeled data and a small number of labeled data, semisupervised sparse feature selection has received increasing attention in recent years. However, most semisupervised feature selection algorithms are developed for single-view data and cannot naturally handle multiview data. Moreover, most existing semisupervised sparse feature selection methods are based on Laplacian regularization, which is a lack of extrapolating power. To overcome the above-mentioned drawbacks, we present a multiview Hessian semisupervised sparse feature selection (MHSFS) framework in this paper. MHSFS can directly accomplish multiview sparse feature selection by exploiting multiview learning to reveal and leverage the correlated and complemental information among different views. In addition, MHSFS can achieve better performance based on Hessian regularization, which favors functions whose values linearly vary with respect to geodesic distance and preserves the local manifold structure well. A simple yet efficient iterative method is proposed to solve the objective function, followed by convergence analysis. We apply the proposed method into different multimedia analysis tasks, such as image annotation, video concept detection, and 3D motion analysis. The results show that MHSFS outperforms the state-of-the-art sparse feature selection methods and achieves good performance.