摘要： Most existing low-rank sparse embedding models extract features of data in the original input space and usually separate the manifold preservation step from the coding process, which may result in the decreased performance. In this paper, a novel Robust Adaptive Low-rank and Sparse Embedding (RALSE) framework is technically proposed for salient feature extraction of the high-dimensional data by seamlessly integrating the joint low-rank and sparse recovery with the robust adaptive salient feature extraction. Specifically, our RALSE integrates the joint low-rank and sparse representation, adaptive neighborhood preserving graph weight learning and the robustness-promoting representation into a unified framework. For accurate similarity measure, RALSE computes the adaptive weights by minimizing the reconstruction error over the noise-removed data and salient features simultaneously, where L1-norm is regularized to ensure the sparse properties of learnt weights. RALSE can also ensure the learnt projection to preserve local neighborhood information of embedded features clearly and adaptively. The projection is not only modeled under joint low-rank and sparse regularization, but also computed from a clean subspace, making it powerful for the salient feature extraction. Thus, the learnt low-rank sparse features would be more accurate for subsequent classification. Extensive results demonstrate the effectiveness of our RALSE formulation for data representation and classification.