4. Unsupervised learning¶
- 4.1. Gaussian mixture models
- 4.1.1. GMM classifier
- 4.1.2. VBGMM classifier: variational Gaussian mixtures
- 4.1.3. DPGMM classifier: Infinite Gaussian mixtures
- 4.1.3.1. Pros and cons of class DPGMM: Diriclet process mixture model
- 4.1.3.2. The Dirichlet Process
- 4.2. Manifold learning
- 4.3. Clustering
- 4.3.1. Overview of clustering methods
- 4.3.2. K-means
- 4.3.3. Affinity Propagation
- 4.3.4. Mean Shift
- 4.3.5. Spectral clustering
- 4.3.6. Hierarchical clustering
- 4.3.7. DBSCAN
- 4.3.8. Clustering performance evaluation
- 4.4. Decomposing signals in components (matrix factorization problems)
- 4.5. Covariance estimation
- 4.6. Novelty and Outlier Detection
- 4.7. Hidden Markov Models