Tensor analysis plays an important role in modern image and vision computing problems. Most of the existing tensor analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based tensor analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach.
|Number of pages||7|
|Journal||IEEE Transactions on Circuits and Systems For Video Technology|
|Early online date||7 Apr 2009|
|Publication status||Published - Feb 2010|
- tensor analysis