Latent Low-Rank Representation for subspace segmentation and feature extraction

Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dictionary, which is a key aspect of LRR. However, such a strategy may depress the performance, especially when the observations are insufficient and/or grossly corrupted. In this… CONTINUE READING

8 Figures & Tables

Topics

Statistics

0501002012201320142015201620172018
Citations per Year

413 Citations

Semantic Scholar estimates that this publication has 413 citations based on the available data.

See our FAQ for additional information.