2. Unsupervised learning¶
- 2.1. Gaussian mixture models
- 2.2. Manifold learning
- 2.2.1. Introduction
- 2.2.2. Isomap
- 2.2.3. Locally Linear Embedding
- 2.2.4. Modified Locally Linear Embedding
- 2.2.5. Hessian Eigenmapping
- 2.2.6. Spectral Embedding
- 2.2.7. Local Tangent Space Alignment
- 2.2.8. Multi-dimensional Scaling (MDS)
- 2.2.9. t-distributed Stochastic Neighbor Embedding (t-SNE)
- 2.2.10. Tips on practical use
- 2.3. Clustering
- 2.4. Biclustering
- 2.5. Decomposing signals in components (matrix factorization problems)
- 2.5.1. Principal component analysis (PCA)
- 2.5.2. Truncated singular value decomposition and latent semantic analysis
- 2.5.3. Dictionary Learning
- 2.5.4. Factor Analysis
- 2.5.5. Independent component analysis (ICA)
- 2.5.6. Non-negative matrix factorization (NMF or NNMF)
- 2.5.7. Latent Dirichlet Allocation (LDA)
- 2.6. Covariance estimation
- 2.7. Novelty and Outlier Detection
- 2.8. Density Estimation
- 2.9. Neural network models (unsupervised)