Using Bilevel Feature Extractors to Reduce Dimensionality in Images

摘要

A bilevel procedure for dimensionality reduction makes it possible to discover the underlying global geometry of a complex natural observations dataset-such as human handwriting or faces under different viewing positions-with higher precision than existing methods.