GATE Machine Learning: Dimensionality reduction and principal component analysis – Previous Year Questions

3 GATE previous year questions on Dimensionality reduction and principal component analysis (Machine Learning, Data Science and Artificial Intelligence) with answers and explanations, from every paper.

1. GATE 2024 DA Q22 (1 mark, Multiple choice) – For any binary classification dataset, let S B Rd d and S W Rd d be the between-class and within-class scatter (covariance) matrices, respectively.…
2. GATE 2025 DA Q60 (2 marks, Numerical answer) – Let D = \x(1), , x(n)\ be a dataset of n observations where each x(i) R100. It is given that i=1n x(i) = 0. The covariance matrix computed from D has…
3. GATE 2026 DA Q11 (1 mark, Multiple choice) – For a classification problem, Principal Component Analysis (PCA) has been used to reduce the dimensionality of a feature space from 100 to 10. Which…