Artificial Intelligence · Machine Learning Foundations
A topic within Machine Learning Foundations, itself one of 15 topics in that field and part of Artificial Intelligence.
2 works
Jonathon Shlens
Derives PCA from first principles as a change of basis that maximises variance and decorrelates the data, building the linear-algebra intuition that most treatments assume the reader already has.
link checked 17 Sept 2026Roweis & Saul
Recovers a low-dimensional manifold by assuming each point can be reconstructed from a weighted combination of its neighbours, and shows that preserving those local weights in fewer dimensions preserves the manifold's global structure.