Unsupervised Learning
Clustering, mixture models and EM, PCA and SVD, manifold embeddings, and density estimation.
7concepts
145flashcards
53minutes of reading
- 01 Clustering Validation: Silhouette, Gap, ARI and Choosing k Internal indices like silhouette and the gap statistic score a clustering against its own geometric ideal, external indices like ARI and NMI need chance correction to mean anything, and Kleinberg's impossibility theorem explains why no index can settle what the right clustering is.
- 02 Gaussian Mixtures and the EM Algorithm How treating the cluster label as a latent variable turns an intractable likelihood into two closed-form steps, why the likelihood is guaranteed to increase, and why it is unbounded above.