Hong Chang, Dit-Yan Yeung
To appear in The Journal of the Pattern Recognition Society
Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. However, they are not robust enough against noise and outliers in the data. In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering under both unsupervised and semi-supervised settings. Our proposed method is significantly more robust than spectral clustering and path-based clustering. We have performed experiments based on both synthetic and real-worl data, comparing our method with some other methods. In particular, color images from the Berkeley Segmentation Dataset and Benchmark are used in the image segmentation experiments. Experimental results show that our method consistently outperforms other methods due to its higher robustness
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