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Spectral Clustering with Perturbed Data
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft

Mon Dec 08 08:45 PM -- 12:00 AM (PST) @

Spectral clustering is useful for a wide-ranging set of applications in areas such as biological data analysis, image processing and data mining. However, the computational and/or communication resources required by the method in processing large-scale data sets are often prohibitively high, and practitioners are often required to perturb the original data in various ways (quantization, downsampling, etc) before invoking a spectral algorithm. In this paper, we use stochastic perturbation theory to study the effects of data perturbation on the performance of spectral clustering. We show that the error under perturbation of spectral clustering is closely related to the perturbation of the eigenvectors of the Laplacian matrix. From this result we derive approximate upper bounds on the clustering error. We show that this bound is tight empirically across a wide range of problems, suggesting that it can be used in practical settings to determine the amount of data reduction allowed in order to meet a specification of permitted loss in clustering performance.

Author Information

Ling Huang (Intel)
Donghui Yan (U. C. Berkeley)
Michael Jordan (UC Berkeley)
Nina Taft

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