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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/25D438D5-51A7-4B8D-9E6D-1D1B4A108D81 | - |
| Identifier | https://papers.nips.cc/paper/6126-robust-k-means-a-theoretical-revisit | - |
| Language | en | - |
| Extent | 9 pages | en |
| Title | Robust k-means: a theoretical revisit | en |
| Creator | Georgogiannis Alexandros | en |
| Creator | Γεωργογιαννης Αλεξανδρος | el |
| Publisher | Neural information processing systems foundation | en |
| Content Summary | Over the last years, many variations of the quadratic k-means clustering procedure have been proposed, all aiming to robustify the performance of the algorithm in the presence of outliers. In general terms, two main approaches have been developed: one based on penalized regularization methods, and one based on trimming functions. In this work, we present a theoretical analysis of the robustness and consistency properties of a variant of the classical quadratic k-means algorithm, the robust k-means, which borrows ideas from outlier detection in regression. We show that two outliers in a dataset are enough to breakdown this clustering procedure. However, if we focus on "well-structured" datasets, then robust k-means can recover the underlying cluster structure in spite of the outliers. Finally, we show that, with slight modifications, the most general non-asymptotic results for consistency of quadratic k-means remain valid for this robust variant. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-10-26 | - |
| Date of Publication | 2016 | - |
| Subject | k-means clustering | en |
| Bibliographic Citation | A. Georgogiannis, "Robust k-means: a theoretical revisit," in 30th Annual Conference on Neural Information Processing Systems, 2016, pp. 2891-2899. | en |
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