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In multi-label learning, objects are essentially related to multiple semantic meanings, and the type of data is confronted with the impact of high feature dimensionality simultaneously, such as the bioinformatics and text mining applications. To tackle the learning problem, the key technology, i.e., feature selection, is developed to reduce dimensionality, whereas most of the previous methods for multi-label feature selection are either directly transformed from traditional single-label feature selection methods or half-baked in the label information exploitation, and thus causing the redundant or irrelevant features involved in the selected feature subset. Aimed to seek discriminative features across multiple class labels, we propose an embedded multi-label feature selection method with manifold regularization. To be specific, a low-dimensional embedding is constructed based on the original feature space to fit the label …