Under logarithmic loss, PCA on heavy-tailed observations from the superstatistical model recovers the principal directions of the underlying Gaussian generator's covariance.
Online robust principal component analysis with change point detection
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Heavy-Tailed Principal Component Analysis
Under logarithmic loss, PCA on heavy-tailed observations from the superstatistical model recovers the principal directions of the underlying Gaussian generator's covariance.