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.
Loading...
Related Quiz
- Which variant of RNN is designed to better capture long-term dependencies in sequence data?
- How does clustering differ from classification?
- Can you discuss the geometric interpretation of Eigenvectors in PCA?
- What is the primary challenge addressed by the multi-armed bandit problem?
- What are the challenges in imbalanced classification problems?