How does LDA differ from Principal Component Analysis (PCA)?

  • LDA and PCA have the same goal and method
  • LDA focuses on unsupervised learning while PCA focuses on supervised learning
  • LDA is concerned with maximizing between-class variance, while PCA focuses on maximizing total variance
  • LDA uses Eigenvalues, while PCA uses Eigenvectors
LDA aims to maximize between-class variance and minimize within-class variance for classification, while PCA focuses on "maximizing total variance" without considering class labels. PCA is used mainly for dimensionality reduction and does not consider class separation.
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