Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Kernel PCA Versus ISOMAP

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Mathematics
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11th Grade - University
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Hard
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the matrix X transpose X represent in PCA?
Pairwise differences
Pairwise similarities
Eigenvalues
Eigenvectors
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary goal of PCA when using the matrix X transpose X?
Preserve data mean
Reduce data dimensions
Minimize data variance
Maximize data variance
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key characteristic of geodesic distances compared to Euclidean distances?
They are computed using dot products
They ignore data structure
They follow the data manifold
They are always shorter
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which algorithm can be used to find the shortest path in a K-nearest neighbor graph?
Dijkstra's algorithm
K-means clustering
Gradient descent
Backpropagation
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is Isomap primarily used for?
Nonlinear dimensionality reduction
Data normalization
Data clustering
Linear dimensionality reduction
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does kernel PCA allow for that traditional PCA does not?
Linear transformations
Nonlinear dimensionality reduction
Data normalization
Data clustering
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In kernel PCA, what is the role of transforming data into a higher-dimensional space?
To increase data variance
To apply linear PCA in a new space
To reduce computation time
To simplify data structure
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