The study considers the scheduling problem of identical parallel machines subject to minimization of the maximumcompletion time and the maximum tardiness expressed in a linear convex objective function. The maximum completiontime or makespan is the date when the last job to be completed leaves the system. The maximum tardiness is indicated bythe job that is completed with the lo...

Subspace learning methods are widely used in background modeling to tackle illumination changes. Their main advantage is that it doesn’t need to label data during the training and running phase. Recently, White et al. [1] have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervise...

Previous works have demonstrated that Laplacian embedding can well preserve the local intrinsic structure. However, it ignores the diversity and may impair the local topology of data. In this paper, we build an objective function to learn the local intrinsic structure that characterizes both the local similarity and diversity of data, and then combine it with global structure to build a scatter...