Autoencoders vs. PCA: I Rigged the Test and PCA Still Won

An experimental comparison was conducted between autoencoders and Principal Component Analysis (PCA) for anomaly detection. The author designed two tests, one with linearly separable anomalies and another with nonlinear anomalies specifically intended to favor the autoencoder. Surprisingly, PCA performed on par with or slightly better than the autoencoder in both scenarios, even the one rigged in the autoencoder's favor. The conclusion is that the theoretical advantage of autoencoders is not automatic and requires significant data, architecture tuning, and engineering effort to realize. A default, untuned autoencoder may not offer a performance benefit over simpler, more data-efficient methods like PCA.