Robust spherical principal curves.

• Propose a new robust principal curve for data sets that deviate from the normality (Gaussian) assumption. • Investigate a theoretical property of the robust principal curve, termed 'stationarity,' which implies that the proposed method is canonical in the spherical domain. • Demonstrate the promising empirical performance of the proposed method through numerical experiments such as simulation study and real data analysis. Principal curves are a nonlinear generalization of principal components and go through the mean of data lying in Euclidean space. In this paper, we propose L 1 -type and Huber-type principal curves through the median of data to robustify the principal curves for a dataset that may contain outliers. We further investigate the stationarity of the proposed robust principal curves on S 2. Results from numerical experiments on S 2 and S 4 , including real data analysis, manifest promising empirical features of the proposed method. [ABSTRACT FROM AUTHOR]