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Multiclass spectral clustering

Yu, Shi·2003·Proceedings Ninth IEEE International Conference on Computer Vision

Abstract

We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigen-decomposition. We clarify the role of eigenvectors as a generator of all optimal solutions through orthonormal transforms. We then solve an optimal discretization problem, which seeks a discrete solution closest to the continuous optima. The discretization is efficiently computed in an iterative fashion using singular value decomposition and nonmaximum suppression. The resulting discrete solutions are nearly global-optimal. Our method is robust to random initialization and converges faster than other clustering methods. Experiments on real image segmentation are reported.

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Crossref · 10.1109/iccv.2003.1238361

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Authors
Yu, & Shi
Venue
Proceedings Ninth IEEE International Conference on Computer Vision
Publisher
IEEE
Published
2003
Pages
313-319 vol.1
OpenAlex
W2135674549
Cited by
947
References
0
FWCI
12.03
Type
conference paper
Language
en
Open access
closed

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