Cross-Validation for Unsupervised Learning

Understand

Cross-validation (CV) is a popular method for model-selection.

• Unfortunately, it is not immediately obvious how to apply CV to unsupervised or exploratory contexts.
• This thesis discusses some extensions of cross-validation to unsupervised learning, specifically focusing on the problem of choosing how many principal components to keep.
• We introduce the latent factor model, define an objective criterion, and show how CV can be used to estimate the intrinsic dimensionality of a data set.