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IDEAL
2010
Springer
14 years 8 months ago
Approximating the Covariance Matrix of GMMs with Low-Rank Perturbations
: Covariance matrices capture correlations that are invaluable in modeling real-life datasets. Using all d2 elements of the covariance (in d dimensions) is costly and could result ...
Malik Magdon-Ismail, Jonathan T. Purnell
NIPS
2004
15 years 17 hour ago
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices
We prove generalization error bounds for predicting entries in a partially observed matrix by fitting the observed entries with a low-rank matrix. In justifying the analysis appro...
Nathan Srebro, Noga Alon, Tommi Jaakkola
79
Voted
CVPR
2010
IEEE
15 years 7 months ago
Robust video denoising using low rank matrix completion
Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, ...
Hui Ji, Chaoqiang Liu, Zuowei Shen, Yuhong Xu