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CVPR
2005
IEEE
14 years 6 months ago
Robust L1 Norm Factorization in the Presence of Outliers and Missing Data by Alternative Convex Programming
Matrix factorization has many applications in computer vision. Singular Value Decomposition (SVD) is the standard algorithm for factorization. When there are outliers and missing ...
Qifa Ke, Takeo Kanade
CVPR
2010
IEEE
13 years 2 months ago
Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the ...
Anders Eriksson, Anton van den Hengel
CVPR
2008
IEEE
14 years 6 months ago
Robust tensor factorization using R1 norm
Over the years, many tensor based algorithms, e.g. two dimensional principle component analysis (2DPCA), two dimensional singular value decomposition (2DSVD), high order SVD, have...
Heng Huang, Chris H. Q. Ding