Under logarithmic loss, PCA on heavy-tailed observations from the superstatistical model recovers the principal directions of the underlying Gaussian generator's covariance.
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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.