Dimensionality Reduction
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Machine Learning
Unsupervised Learning
Interview questions on Unsupervised Learning.
20 questions
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Dimensionality Reduction
Q2. List down the two main approaches for dimensionality reduction?
Dimensionality Reduction
Q3. What are some linear techniques of dimensionality reduction?
Dimensionality Reduction
Q4. What is the benefit of using linear methods for dimensionality reduction?
Dimensionality Reduction
Q5. What is the benefit of using linear methods for dimensionality reduction? (Part 2)
Dimensionality Reduction
Q6. What are the drawbacks of using linear methods for dimensionality reduction?
Dimensionality Reduction
Q7. What are the benefits of using nonlinear methods in dimensionality reduction?
Dimensionality Reduction
Q8. What are some non-linear techniques of dimensionality reduction?
Dimensionality Reduction
Q9. Eigen decomposition is a common factorization technique used for dimensionality reduction. Is the eigen decomposition of a matrix always unique?
Dimensionality Reduction
Q10. Name some applications of eigenvalues and eigenvectors.
Dimensionality Reduction
Q11. We want to do PCA on a dataset of multiple features in different ranges. For example, one is in the range $0-1$ and one is in the range $10 - 1000$. Will PCA work on this dataset?
Dimensionality Reduction
Q12. Under what conditions can one apply eigen decomposition? What about SVD?
- What is the relationship between SVD and eigen decomposition?
- What’s the relationship between PCA and SVD?
Dimensionality Reduction
Q13. How does $t-SNE$ (T-distributed Stochastic Neighbor Embedding) work? Why do we need it?
Dimensionality Reduction
Q14. Is it good to use PCA as a feature selection method?
Dimensionality Reduction
Q15. Is PCA a linear model or non-linear model?
Dimensionality Reduction
Q16. What is the importance of eigenvalues and eigenvectors in PCA?
Dimensionality Reduction
Q17. How do you decide the number of principal components to retain in PCA?
Dimensionality Reduction
Q18. What is the difference between PCA and Linear Discriminant Analysis (LDA)?
Dimensionality Reduction
Q19. What are the limitations of PCA?
Dimensionality Reduction