13. Model-based Feature Extraction

Topics, Class Notes and Code Assignments and Side Readings
  • Feature Selection Methods
    • Filter Methods
    • Wrapper Methods
    • Embedded Methods
  • Feature Extract Methods
    • Clustering Methods
    • PCA Methods
    • Linear Discriminant Analysis (LDA)
    • Other Model-based Methods Such As LOF Scores, etc.
  • Feature Selection and Extraction for Serial Data
  • Notes and code
  • Optional Reading Assignments
    • A Review of feature selection methods [PDF]
    • A monograph of feature extraction [PDF eBook]
    • Documentation of R library{FeatureExtraction} [PDF]
  • Written Assignment (This week's assignment is optional!)
    • Pick one or more feature extraction (prefer model-based) method(s) to implement and add the results to your project #2: EDA and Feature Engineering.