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» Robust tensor factorization using R1 norm
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CVPR
2008
IEEE
15 years 23 days 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
ICML
2006
IEEE
14 years 11 months ago
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...
Chris H. Q. Ding, Ding Zhou, Xiaofeng He, Hongyuan...
ISCAS
2008
IEEE
217views Hardware» more  ISCAS 2008»
14 years 5 months ago
Approximate L0 constrained non-negative matrix and tensor factorization
— Non-negative matrix factorization (NMF), i.e. V ≈ WH where both V, W and H are non-negative has become a widely used blind source separation technique due to its part based r...
Morten Mørup, Kristoffer Hougaard Madsen, L...
ICCV
2007
IEEE
15 years 21 days ago
Robust Visual Tracking Based on Incremental Tensor Subspace Learning
Most existing subspace analysis-based tracking algorithms utilize a flattened vector to represent a target, resulting in a high dimensional data learning problem. Recently, subspa...
Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, ...
CVPR
2005
IEEE
15 years 23 days 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