What are the main differences between PCA and Linear Discriminant Analysis (LDA) as techniques for dimensionality reduction?

  • Both techniques work the same way
  • PCA is a type of LDA
  • PCA is unsupervised, LDA is supervised
  • PCA maximizes within-class variance, LDA between
The main difference between PCA and LDA is that PCA is an unsupervised technique that maximizes the total variance in the data, while LDA is a supervised technique that maximizes the between-class variance and minimizes the within-class variance. This makes LDA more suitable when class labels are available, while PCA can be used without them.
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