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Multiclass spectral clustering

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2 Author(s)
Yu, S.X. ; Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Shi, J.

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.

Published in:

Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on

Date of Conference:

13-16 Oct. 2003