2. Unsupervised learningΒΆ
- 2.1. Gaussian mixture models
- 2.2. Manifold learning
- 2.3. Clustering
- 2.3.1. Overview of clustering methods
- 2.3.2. K-means
- 2.3.3. Affinity Propagation
- 2.3.4. Mean Shift
- 2.3.5. Spectral clustering
- 2.3.6. Hierarchical clustering
- 2.3.7. DBSCAN
- 2.3.8. Clustering performance evaluation
- 2.4. Biclustering
- 2.5. Decomposing signals in components (matrix factorization problems)
- 2.6. Covariance estimation
- 2.7. Novelty and Outlier Detection
- 2.8. Hidden Markov Models
- 2.9. Density Estimation
- 2.10. Neural network models (unsupervised)