4. Dataset transformations

• 4.1. Feature extraction
  • 4.1.1. Loading features from dicts
  • 4.1.2. Feature hashing
    • 4.1.2.1. Implementation details
  • 4.1.3. Text feature extraction
    • 4.1.3.1. The Bag of Words representation
    • 4.1.3.2. Sparsity
    • 4.1.3.3. Common Vectorizer usage
    • 4.1.3.4. Tf–idf term weighting
    • 4.1.3.5. Decoding text files
    • 4.1.3.6. Applications and examples
    • 4.1.3.7. Limitations of the Bag of Words representation
    • 4.1.3.8. Vectorizing a large text corpus with the hashing trick
    • 4.1.3.9. Performing out-of-core scaling with HashingVectorizer
    • 4.1.3.10. Customizing the vectorizer classes
  • 4.1.4. Image feature extraction
    • 4.1.4.1. Patch extraction
    • 4.1.4.2. Connectivity graph of an image
• 4.2. Preprocessing data
  • 4.2.1. Standardization, or mean removal and variance scaling
    • 4.2.1.1. Scaling features to a range
    • 4.2.1.2. Centering kernel matrices
  • 4.2.2. Normalization
  • 4.2.3. Binarization
    • 4.2.3.1. Feature binarization
  • 4.2.4. Encoding categorical features
  • 4.2.5. Label preprocessing
    • 4.2.5.1. Label binarization
    • 4.2.5.2. Label encoding
  • 4.2.6. Imputation of missing values
  • 4.2.7. Unsupervised data reduction
    • 4.2.7.1. PCA: principal component analysis
    • 4.2.7.2. Random projections
    • 4.2.7.3. Feature agglometration
• 4.3. Kernel Approximation
  • 4.3.1. Nystroem Method for Kernel Approximation
  • 4.3.2. Radial Basis Function Kernel
  • 4.3.3. Additive Chi Squared Kernel
  • 4.3.4. Skewed Chi Squared Kernel
  • 4.3.5. Mathematical Details
• 4.4. Random Projection
  • 4.4.1. The Johnson-Lindenstrauss lemma
  • 4.4.2. Gaussian random projection
  • 4.4.3. Sparse random projection
• 4.5. Pairwise metrics, Affinities and Kernels
  • 4.5.1. Cosine similarity
  • 4.5.2. Chi Squared Kernel