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2 results for “dimensionality reduction”
Foundations
Autoencoder
An autoencoder is a type of artificial neural network trained to reconstruct its input through a compressed internal representation, used for dimensionality reduction, feature learning, and anomaly detection.
5 min readUpdated May 2026
Foundations
Principal Component Analysis
An unsupervised statistical technique that transforms correlated variables into a smaller set of uncorrelated components that preserve as much variance in the original data as possible.
4 min readUpdated May 2026