Unsupervised Learning
Clustering, mixture models and EM, PCA and SVD, manifold embeddings, and density estimation.
4concepts
62flashcards
30minutes of reading
- 01 PCA, SVD and Whitening Why maximising retained variance and minimising reconstruction error give the same answer, how the SVD computes it without ever forming a covariance matrix, and what whitening destroys.
- 02 UMAP, t-SNE and What They Distort Neighbour embeddings optimise local neighbourhood preservation and nothing else, which makes cluster separation, cluster size and inter-cluster distance in the resulting picture largely uninterpretable.