84 search results - page 6 / 17 » Robust Matrix Decomposition with Outliers |
ICML
2006
IEEE
15 years 10 months ago
2006
IEEE
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...
112
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ECCV
2004
Springer
15 years 11 months ago
2004
Springer
Low-level image processing algorithms generally provide noisy features that are far from being Gaussian. Medium-level tasks such as object detection must therefore be robust to out...
121
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KDD
2012
ACM
13 years 8 days ago
2012
ACM
Multi-task learning (MTL) aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. Recently, multi-task feature learning alg...
CVPR
1998
IEEE
15 years 12 months ago
1998
IEEE
This paper addresses robust feature tracking. We extend the well-known Shi-Tomasi-Kanade tracker by introducing an automatic scheme for rejecting spurious features. We employ a si...
TSP
2008
14 years 9 months ago
2008
Abstract--Low-rank matrix approximation has applications in many fields, such as 2D filter design and 3D reconstruction from an image sequence. In this paper, one issue with low-ra...