Unsupervised Learning
4concepts
31flashcards
30minutes of reading
- 01 K-Means and Its Assumptions Lloyd's algorithm is coordinate descent on a squared-error objective, which explains why it always converges, why it converges to the wrong answer without careful seeding, and the exact cluster shapes it cannot represent.
- 02 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.
- 03 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.
- 04 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.