2. Unsupervised learning

• 2.1. Gaussian mixture models
  • 2.1.1. Gaussian Mixture
  • 2.1.2. Variational Bayesian Gaussian Mixture
• 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
  • 2.3.1. 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. HDBSCAN
  • 2.3.9. OPTICS
  • 2.3.10. BIRCH
  • 2.3.11. Clustering performance evaluation
• 2.4. Biclustering
  • 2.4.1. Spectral Co-Clustering
  • 2.4.2. Spectral Biclustering
  • 2.4.3. Biclustering evaluation
• 2.5. Decomposing signals in components (matrix factorization problems)
  • 2.5.1. Principal component analysis (PCA)
  • 2.5.2. Kernel Principal Component Analysis (kPCA)
  • 2.5.3. Truncated singular value decomposition and latent semantic analysis
  • 2.5.4. Dictionary Learning
  • 2.5.5. Factor Analysis
  • 2.5.6. Independent component analysis (ICA)
  • 2.5.7. Non-negative matrix factorization (NMF or NNMF)
  • 2.5.8. Latent Dirichlet Allocation (LDA)
• 2.6. Covariance estimation
  • 2.6.1. Empirical covariance
  • 2.6.2. Shrunk Covariance
  • 2.6.3. Sparse inverse covariance
  • 2.6.4. Robust Covariance Estimation
• 2.7. Novelty and Outlier Detection
  • 2.7.1. Overview of outlier detection methods
  • 2.7.2. Novelty Detection
  • 2.7.3. Outlier Detection
  • 2.7.4. Novelty detection with Local Outlier Factor
• 2.8. Density Estimation
  • 2.8.1. Density Estimation: Histograms
  • 2.8.2. Kernel Density Estimation
• 2.9. Neural network models (unsupervised)
  • 2.9.1. Restricted Boltzmann machines