Subspace Clustering with Priors via Sparse Quadratically Constrained Quadratic Programming

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem can be formulated as a convex semi-definite optimization problem subject to an additional rank constrain that involves only a very sm...

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Vydané v:	2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) s. 5204 - 5212
Hlavní autori:	Yongfang Cheng, Yin Wang, Sznaier, Mario, Camps, Octavia
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2016
Predmet:	Clustering algorithms Computational complexity Image segmentation Noise measurement Robustness Symmetric matrices
ISSN:	1063-6919
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