2. Unsupervised learning

• 2.1. Gaussian mixture models
  • 2.1.2. Variational Bayesian Gaussian Mixture
• • 2.2.1. Introduction
  • 2.2.3. Locally Linear Embedding
  • 2.2.5. Hessian Eigenmapping
  • 2.2.7. Local Tangent Space Alignment
  • 2.2.9. t-distributed Stochastic Neighbor Embedding (t-SNE)
• 2.3. Clustering
  • 2.3.3. Affinity Propagation
  • 2.3.5. Spectral clustering
  • 2.3.7. DBSCAN
  • 2.3.9. Birch
• 2.4. Biclustering
  • 2.4.2. Spectral Biclustering
• 2.5. Decomposing signals in components (matrix factorization problems)
  • 2.5.2. Truncated singular value decomposition and latent semantic analysis
  • 2.5.5. Independent component analysis (ICA)
  • 2.5.7. Latent Dirichlet Allocation (LDA)
• • 2.6.1. Empirical covariance
  • 2.6.3. Sparse inverse covariance
• 2.7. Novelty and Outlier Detection
  • 2.7.2. Novelty Detection
  • 2.7.4. Novelty detection with Local Outlier Factor
• • 2.8.1. Density Estimation: Histograms