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- W2103759851 abstract "Although spectral clustering has enjoyed considerable empirical success in machine learning, its theoretical properties are not yet fully developed. We analyze the performance of a spectral algorithm for hierarchical clustering and show that on a class of hierarchically structured similarity matrices, this algorithm can tolerate noise that grows with the number of data points while still perfectly recovering the hierarchical clusters with high probability. We additionally improve upon previous results for k-way spectral clustering to derive conditions under which spectral clustering makes no mistakes. Further, using minimax analysis, we derive tight upper and lower bounds for the clustering problem and compare the performance of spectral clustering to these information theoretic limits. We also present experiments on simulated and real world data illustrating our results." @default.
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- W2103759851 date "2011-12-12" @default.
- W2103759851 modified "2023-09-25" @default.
- W2103759851 title "Noise Thresholds for Spectral Clustering" @default.
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- W2103759851 doi "https://doi.org/10.1184/r1/6475928.v1" @default.
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