Learning From Weakly Labeled Data Based on Manifold Regularized Sparse Model.In multilabel learning, each training example is represented by a single instance, which is relevant to multiple class labels simultaneously.  To be specific, an optimization framework is constructed based on the manifold regularized sparse model, in which the correlations among labels and feature structure are considered to model global and local label correlations, thereby achieving discriminative feature analysis for mapping training data to ground-truth label space. 
Manifold Regularized Sparse Model
Learning From Weakly Labeled Data Based on Manifold Regularized Sparse Model.In multilabel learning, each training example is represented by a single instance, which is relevant to multiple class labels simultaneously.  To be specific, an optimization framework is constructed based on the manifold regularized sparse model, in which the correlations among labels and feature structure are considered to model global and local label correlations, thereby achieving discriminative feature analysis for mapping training data to ground-truth label space. 
Manifold Regularized Sparse Model