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Improved spectral clustering based on Nystrom method
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DOI：10.1007/s11042-017-4566-4.png)
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摘要
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Spectral graph clustering methods have been a hot topic in the field of image segmentation. However, because the computational demands are needed for Spectral graph clustering methods, it has been severely limited to apply them into large data sets, such as high resolution image. It would be too expensive or even impractical for spectral decomposition to provide the optimal approximation in dealing with large or high-dimensional datasets. A novel approach aiming at reducing the computational requirements is proposed in this paper. Our approach focuses on Nystrom method for the solution of eigen-function problems. This approach enables us to use a small number of samples to infer the overall clustering solution. Based on the proposed Nystrom sampling method, this paper presents a spectral clustering algorithm for massive data analysis, and the experiments show the method is both feasible and effective.
Keyword:
Big data
Spectral clustering
Eigen-decomposition
Nystrom extension
Incremental sampling
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3
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被引数:
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