Senior interview questionAsked at C3 AI · interview write-up
Compare PCA and autoencoders as ways to fight the curse of dimensionality, and explain how you'd tell which compression helps downstream.
Model answer
In high dimensions data gets sparse and distances concentrate, so models need far more data. PCA finds the linear projection that keeps the most variance: fast, deterministic, and easy to size from explained variance. An autoencoder learns a nonlinear encoder and decoder by minimising reconstruction error. A linear autoencoder with squared loss spans the same subspace as PCA, so it only wins on nonlinear structure with enough data. Judge them on the downstream task, not reconstruction: train the same model on each representation, sweep the dimension, and compare held-out accuracy and stability across seeds.
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