4. Unsupervised learning

• 4.1. Gaussian mixture models
  • 4.1.1. GMM classifier
• 4.2. Clustering
  • 4.2.1. K-means
  • 4.2.2. Affinity propagation
  • 4.2.3. Mean Shift
  • 4.2.4. Spectral clustering
  • 4.2.5. Hierarchical clustering
    • 4.2.5.1. Adding connectivity constraints
• 4.3. Decomposing signals in components (matrix factorization problems)
  • 4.3.1. Principal component analysis (PCA)
    • 4.3.1.1. Exact PCA and probabilistic interpretation
    • 4.3.1.2. Approximate PCA
    • 4.3.1.3. Kernel PCA
  • 4.3.2. Independent component analysis (ICA)
  • 4.3.3. Non-negative matrix factorization (NMF)
• 4.4. Covariance estimation
  • 4.4.1. Empirical covariance
  • 4.4.2. Shrunk Covariance
    • 4.4.2.1. Basic shrinkage
    • 4.4.2.2. Ledoit-Wolf shrinkage
    • 4.4.2.3. Oracle Approximating Shrinkage