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- W2952558719 abstract "Convex clustering has gained popularity recently due to its desirable performance in empirical studies. It involves solving a convex optimization problem with the cost function being a squared error loss plus a fusion penalty that encourages the estimated centroids for observations in the same cluster to be identical. However, when data are contaminated, convex clustering with a squared error loss will fail to identify correct cluster memberships even when there is only one arbitrary outlier. To address this challenge, we propose a robust convex clustering method. Theoretically, we show that the new estimator is resistant to arbitrary outliers: it does not break down until more than half of the observations are arbitrary outliers. In particular, we observe a new phenomenon that the fusion penalty can help enhance robustness. Numerical studies are performed to demonstrate the competitive performance of the proposed method." @default.
- W2952558719 created "2019-06-27" @default.
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- W2952558719 creator A5060567464 @default.
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- W2952558719 date "2019-06-23" @default.
- W2952558719 modified "2023-09-27" @default.
- W2952558719 title "Robust convex clustering: How does fusion penalty enhance robustness?" @default.
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