Robust Subspace Discovery through Supervised Low-Rank Constraints

@inproceedings{Li2014RobustSD,
  title={Robust Subspace Discovery through Supervised Low-Rank Constraints},
  author={Sheng Li and Yun Fu},
  booktitle={SDM},
  year={2014}
}
Subspace learning is a popular approach for feature extraction and classification. However, its performance would be heavily degraded when data are corrupted by large amounts of noise. Inspired by recent work in matrix recovery, we tackle this problem by exploiting a subspace that is robust to noise and large variability for classification. Specifically, we propose a novel Supervised Regularization based Robust Subspace (SRRS) approach via low-rank learning. Unlike existing subspace methods… Expand
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