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IDEAL
2010
Springer
14 years 11 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
89
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NIPS
2004
15 years 3 months 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
CVPR
2010
IEEE
15 years 10 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