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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Veröffentlicht in:	2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) S. 5204 - 5212
Hauptverfasser:	Yongfang Cheng, Yin Wang, Sznaier, Mario, Camps, Octavia
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2016
Schlagworte:	Clustering algorithms Computational complexity Image segmentation Noise measurement Robustness Symmetric matrices
ISSN:	1063-6919
Online-Zugang:	Volltext
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