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Multiclass Spectral Clustering
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Stella X. Yu and Jianbo Shi
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International Conference on Computer Vision, Nice, France, 11-17 Oct 2003
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Paper
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Slides
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Abstract
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We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigendecomposition. 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 non-maximum 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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Keywords
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clustering, spectral graph partitioning, kmeans, image segmentation
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