Robust Low-rank subspace segmentation with finite mixture noise

Xianglin Guo
Xianglin Guo
Xingyu Xie
Xingyu Xie

Pattern Recognition, pp. 55-67, 2019.

Cited by: 3|Bibtex|Views44|DOI:https://doi.org/10.1016/j.patcog.2019.03.028
EI
Other Links: academic.microsoft.com|dblp.uni-trier.de

Abstract:

Abstract Subspace segmentation or clustering remains a challenge of interest in computer vision when handling complex noise existing in high-dimensional data. Most of the current sparse representation or minimum-rank based techniques are constructed on l 1 -norm or l 2 -norm losses, which is sensitive to outliers. Finite mixture model, ...More

Code:

Data:

Your rating :
0

 

Tags
Comments