## Autoencoders vs. PCA: I Rigged the Test and PCA Still Won A theoretical advantage that didn't survive contact with a real benchmark. ## The theoretical case for autoencoders Autoencoders — neural networks trained to reconstruct their own input through a compressed bottleneck — are a standard recommendation for anomaly detection. The theoretical argument is clean: train the network only on normal data, and it learns to reconstruct normal patterns well. Feed it an anomaly, and reconstruction error spikes, because the network never learned to compress that kind of pattern. Unlike PCA, which…