Machine Learning

  UNIT 5 (A) Dimensionality Reduction 1.      The Curse of Dimensionality 2.      Main Approaches for Dimensionality Reduction 3.      Projection 4.      PCA -------------------------------------------------------------------------------------------------------------------------------- Long Answer Questions 1.      What is the curse of dimensionality? 2.      What are the main motivations for reducing a dataset’s dimensionality? What are the main drawbacks? 3.      Explain Projection with example. 4.      Explain PCA with example. Short Answer Questions 1.      Define Dimensionality Reduction. 2.      List main approaches to dimensionality reduction. 3.      List the most popular dimensionality reduction techniques.

                                                                                                   Unit V (A) Dimensionality Reduction 1.       The Curse of Dimensionality 2.       Main Approaches for Dimensionality Reduction 3.       Projection 4.       PCA

                                                                                                      Unit V Dimensionality Reduction 1.      The Curse of Dimensionality 2.      Main Approaches for Dimensionality Reduction 3.      Projection 4.      PCA