Dimensionality Reduction Algorithms MCQs
1. Dimensionality reduction techniques are used to:
a. Increase the number of features in a dataset
b. Reduce the number of features in a dataset
c. Scale the features in a dataset
d. Balance the class distribution in a dataset
Answer: b. Reduce the number of features in a dataset
2. Principal Component Analysis (PCA) is a dimensionality reduction technique that:
a. Maximizes the variance in the original features
b. Minimizes the correlation between features
c. Creates new features that are linear combinations of the original features
d. Normalizes the features in a dataset
Answer: c. Creates new features that are linear combinations of the original features
3. PCA finds the directions of maximum variance in the data by:
a. Singular Value Decomposition (SVD)
b. Eigendecomposition
c. Correlation analysis
d. T-distributed Stochastic Neighbor Embedding (t-SNE)
Answer: b. Eigendecomposition
4. Linear Discriminant Analysis (LDA) is a dimensionality reduction technique that:
a. Maximizes the variance in the original features
b. Minimizes the correlation between features
c. Creates new features that are linear combinations of the original features
d. Maximizes the separability between different classes
Answer: d. Maximizes the separability between different classes
5. Which dimensionality reduction technique is suitable for supervised learning tasks?
a. PCA
b. LDA
c. t-SNE
d. Autoencoder
Answer: b. LDA
6. t-SNE (t-distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique that:
a. Preserves the global structure of the data
b. Reduces the dimensionality based on class labels
c. Focuses on preserving local similarities between data points
d. Performs feature extraction using neural networks
Answer: c. Focuses on preserving local similarities between data points
7. Non-negative Matrix Factorization (NMF) is a dimensionality reduction technique that:
a. Finds orthogonal features that capture the most variance
b. Creates non-linear combinations of the original features
c. Performs feature extraction using neural networks
d. Decomposes a non-negative matrix into two non-negative matrices
Answer: d. Decomposes a non-negative matrix into two non-negative matrices
8. Independent Component Analysis (ICA) is a dimensionality reduction technique that:
a. Finds orthogonal features that capture the most variance
b. Creates non-linear combinations of the original features
c. Performs feature extraction using neural networks
d. Separates a mixture of signals into statistically independent components
Answer: d. Separates a mixture of signals into statistically independent components
9. Manifold Learning is a dimensionality reduction technique that:
a. Creates non-linear combinations of the original features
b. Decomposes a non-negative matrix into two non-negative matrices
c. Finds orthogonal features that capture the most variance
d. Preserves the intrinsic structure of the data in a lower-dimensional space
Answer: d. Preserves the intrinsic structure of the data in a lower-dimensional space
10. Which dimensionality reduction technique can handle both linear and non-linear relationships in the data?
a. PCA
b. LDA
c. t-SNE
d. NMF
Answer: c. t-SNE
11. Which dimensionality reduction technique is based
on maximizing the mutual information between the transformed features and the target variable?
a. PCA
b. LDA
c. t-SNE
d. Mutual Information-based Feature Selection
Answer: b. LDA
12. Which dimensionality reduction technique is based on preserving the pairwise distances between data points?
a. PCA
b. LDA
c. t-SNE
d. NMF
Answer: c. t-SNE
13. Which dimensionality reduction technique is commonly used in image and signal processing?
a. PCA
b. LDA
c. t-SNE
d. ICA
Answer: d. ICA
14. Which dimensionality reduction technique can handle both numerical and categorical features?
a. PCA
b. LDA
c. t-SNE
d. Multiple Correspondence Analysis
Answer: d. Multiple Correspondence Analysis
15. Which dimensionality reduction technique is computationally expensive and suitable for small to medium-sized datasets?
a. PCA
b. LDA
c. t-SNE
d. NMF
Answer: c. t-SNE
16. Which dimensionality reduction technique can be used for feature extraction and denoising of data?
a. PCA
b. LDA
c. t-SNE
d. Autoencoder
Answer: d. Autoencoder
17. Which dimensionality reduction technique is based on the assumption that the data lies on a low-dimensional manifold embedded in a high-dimensional space?
a. PCA
b. LDA
c. t-SNE
d. Manifold Learning
Answer: d. Manifold Learning
18. Which dimensionality reduction technique is suitable for outlier detection and novelty detection?
a. PCA
b. LDA
c. t-SNE
d. Isolation Forest
Answer: d. Isolation Forest
19. Which dimensionality reduction technique is based on maximizing the variance in the transformed features?
a. PCA
b. LDA
c. t-SNE
d. ICA
Answer: a. PCA
20. Which dimensionality reduction technique is suitable for feature selection based on the statistical dependence between features and the target variable?
a. PCA
b. LDA
c. t-SNE
d. Mutual Information-based Feature Selection
Answer: d. Mutual Information-based Feature Selection
21. Which dimensionality reduction technique can be used for visualization of high-dimensional data?
a. PCA
b. LDA
c. t-SNE
d. NMF
Answer: c. t-SNE
22. Which dimensionality reduction technique is based on finding the optimal low-dimensional representation that minimizes the reconstruction error of the original data?
a. PCA
b. LDA
c. t-SNE
d. Autoencoder
Answer: d. Autoencoder
23. Which dimensionality reduction technique is suitable for text mining and natural language processing tasks?
a. PCA
b. LDA
c. t-SNE
d. Latent Semantic Analysis
Answer: d. Latent Semantic Analysis
24. Which dimensionality reduction technique is based on linear projections of the data onto a lower-dimensional subspace?
a. PCA
b. LDA
c. t-SNE
d. ICA
Answer: a. PCA
25. Which dimensionality reduction technique can handle categorical variables and preserve the associations between them?
a. PCA
b. LDA
c. t-SNE
d. Multiple Correspondence Analysis
Answer: d. Multiple Correspondence Analysis
26. Which dimensionality reduction technique is suitable for clustering analysis and outlier detection?
a. PCA
b. LDA
c. t-SNE
d. Isomap
Answer: d. Isomap
27. Which dimensionality reduction technique is based on maximizing the Fisher's discriminant ratio between classes?
a. PCA
b. LDA
c. t-SNE
d. Canonical Correlation Analysis
Answer: b. LDA
28. Which dimensionality reduction technique is suitable for exploring the relationships between multiple variables?
a. PCA
b. LDA
c. t-SNE
d. Factor Analysis
Answer: d. Factor Analysis
29. Which dimensionality reduction technique is based on clustering similar data points into groups and assigning representative prototypes?
a. PCA
b. LDA
c. t-SNE
d. K-Means Clustering
Answer: d. K-Means Clustering
30. Which dimensionality reduction technique is based on optimizing a non-linear manifold that preserves the local neighborhood relationships between data points?
a. PCA
b. LDA
c. t-SNE
d. Isomap
Answer: d. Isomap