Robust convex clustering: How does fusion penalty enhance robustness?

06/23/2019
by   Chenyu Liu, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset