Robust auto-weighted projective low-rank and sparse recovery for visual representation

Neural Networks, pp. 201-215, 2019.

Cited by: 15|Bibtex|Views63|DOI:https://doi.org/10.1016/j.neunet.2019.05.007
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com|dblp.uni-trier.de|www.sciencedirect.com

Abstract:

Most existing low-rank and sparse representation models cannot preserve the local manifold structures of samples adaptively, or separate the locality preservation from the coding process, which may result in the decreased performance. In this paper, we propose an inductive Robust Auto-weighted Low-Rank and Sparse Representation (RALSR) fr...More

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