Linear Discriminant Analysis (LDA) vs PCA — the difference.
mediumAnswer
- PCA is unsupervised — max variance.
- LDA is supervised — projects to maximize class separability: max , where scatter, scatter.
- Yields at most (k-1) components for k classes.
- Uses: dimensionality reduction as preprocessing for classifiers, face recognition (Fisherfaces).
- Assumes normally distributed classes with equal covariance.
Check yourself — multiple choice
- Same as PCA
- PCA unsupervised max variance; LDA supervised max with ≤ k-1 components; assumes equal-cov Gaussians
- Random
- Only for regression
LDA: supervised max between/within scatter; ≤ k-1 components; equal-cov Gaussian.
#dimensionality-reduction
Practise Unsupervised Learning
214 interview questions in this topic.