Connection Between PCA and LDA/QDA

Requirements Clarification & Assessment

• Understanding the Techniques:

  • PCA (Principal Component Analysis): Unsupervised technique focusing on reducing data dimensionality while preserving variance.
  • LDA (Linear Discriminant Analysis): Supervised technique aimed at maximizing class separability by projecting data onto a lower-dimensional space.
  • QDA (Quadratic Discriminant Analysis): An extension of LDA that allows for class-specific covariance matrices, offering more flexibility in class separation.

• Objective:

  • To elucidate the relationship and differences between PCA, LDA, and QDA.
  • To provide insights into when and why each technique is used.

• Contextual Use:

  • Consider scenarios where dimensionality reduction is critical, such as in high-dimensional datasets or when improving computational efficiency.
  • Explore classification tasks where class separability is crucial for model performance.