Dong-Xu Yang

Spatial-sign-based multilinear principal component analysis for tensor data

Simulations and an empirical application show that SMPCA is more accurate and stable than competitors under heavy-tailed distributions and outlier contamination, while retaining competitive performance under light-tailed settings.