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Noisy Dual Principal Component Pursuit

Tianyu Ding · Zhihui Zhu · Tianjiao Ding · Yunchen Yang · Daniel Robinson · Manolis Tsakiris · Rene Vidal

Pacific Ballroom #188

Keywords: [ Unsupervised Learning ] [ Sparsity and Compressed Sensing ] [ Non-convex Optimization ] [ Dimensionality Reduction ] [ Clustering ]


Dual Principal Component Pursuit (DPCP) is a recently proposed non-convex optimization based method for learning subspaces of high relative dimension from noiseless datasets contaminated by as many outliers as the square of the number of inliers. Experimentally, DPCP has proved to be robust to noise and outperform the popular RANSAC on 3D vision tasks such as road plane detection and relative poses estimation from three views. This paper extends the global optimality and convergence theory of DPCP to the case of data corrupted by noise, and further demonstrates its robustness using synthetic and real data.

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