48 Must-Know PCA Interview Questions and Answers 2024

Principal Component Analysis Basics

1.
What is Principal Component Analysis (PCA)?
Answer:
2.
How is PCA used for dimensionality reduction?
Answer:
3.
Can you explain the concept of eigenvalues and eigenvectors in PCA?
Answer:
4.
Describe the role of the covariance matrix in PCA.
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5.
What is the variance explained by a principal component?
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6.
How does scaling of features affect PCA?
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7.
What is the difference between PCA and Factor Analysis?
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8.
Why is PCA considered an unsupervised technique?
Answer:

Mathematical Foundations

9.
Derive the PCA from the optimization perspective, i.e., minimization of reconstruction error.
Answer:
10.
Can you explain the Singular Value Decomposition (SVD) and its relationship with PCA?
Answer:
11.
How do you determine the number of principal components to use?
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12.
What is meant by ‘loading’ in the context of PCA?
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13.
Explain the process of eigenvalue decomposition in PCA.
Answer:
14.
Discuss the importance of the trace of a matrix in the context of PCA.
Answer:

PCA in Practice

15.
What are the limitations of PCA when it comes to handling non-linear relationships?
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16.
Provide examples of how PCA can be used in image processing.
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17.
Explain the curse of dimensionality and how PCA can help to mitigate it.
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18.
How does PCA handle missing values in the data?
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19.
Discuss the application of PCA in feature engineering.
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20.
What is the difference between PCA and t-SNE for dimensionality reduction?
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Algorithm Understanding and Application

21.
Explain how PCA can be used as a noise reduction technique.
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22.
Describe how you would apply PCA for visualization purposes.
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23.
Discuss how PCA can suffer from outlier sensitivity and ways to address it.
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24.
Can PCA be applied to categorical data? Why or why not?
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25.
What are the advantages and drawbacks of kernel PCA compared to linear PCA?
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Coding Challenges

26.
Write a Python function to perform PCA from scratch using NumPy.
Answer:
27.
Use scikit-learn to apply PCA on a high-dimensional dataset and interpret the results.
Answer:
28.
Code a Python script to visualize the eigenfaces from a given set of facial images dataset using PCA.
Answer:
29.
Implement PCA for feature extraction before applying a machine learning model.
Answer:
42.
Implement PCA in TensorFlow or PyTorch and compare the results with scikit-learn’s implementation.
Answer:
43.
Demonstrate how to choose an optimal number of dimensions with PCA in Python using the “elbow method”.
Answer:
44.
Write a Python script to automatically remove outliers before performing PCA.
Answer:
45.
Create a synthetic dataset and show the effect of PCA on classification accuracy using a machine learning algorithm before and after PCA.
Answer:

Case Studies and Scenario-Based Questions

30.
How would you use PCA for data compression in a real-time streaming application?
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31.
Explain how you would apply PCA in a stock market data analysis situation.
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32.
Describe a scenario where using PCA might be detrimental to the performance of a machine learning model.
Answer:
46.
How would you decide whether to use PCA or a classification algorithm for a given dataset?
Answer:
47.
Discuss a case where PCA helped improve model performance by reducing overfitting.
Answer:
48.
Give an example of how PCA might be incorrectly applied to a dataset and propose a solution.
Answer:

Implementation and Practical Considerations

33.
Discuss how you would ensure the robustness of PCA results against variations in the dataset.
Answer:
34.
How can PCA be parallelized to handle very large datasets?
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35.
What are the best practices in visualizing the components obtained from PCA?
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Specific Algorithms and Techniques

36.
Compare the use of PCA to select features with other feature selection methods.
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37.
Explain how Incremental PCA differs from standard PCA and when you would use it.
Answer:
38.
Discuss how Randomized PCA is used and its benefits over traditional PCA.
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Advanced Topics and Research

39.
What recent advancements have been made concerning PCA for big data?
Answer:
40.
Discuss how robust PCA attempts to handle outliers and its practical implications.
Answer:
41.
How does Generalized PCA differ from standard PCA and what are its applications?
Answer:

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48 PCA interview questions

What is Principal Component Analysis (PCA)?

How is PCA used for dimensionality reduction?

Can you explain the concept of eigenvalues and eigenvectors in PCA?

Describe the role of the covariance matrix in PCA.

What is the variance explained by a principal component?

How does scaling of features affect PCA?

What is the difference between PCA and Factor Analysis?

Why is PCA considered an unsupervised technique?

Derive the PCA from the optimization perspective, i.e., minimization of reconstruction error.

Can you explain the Singular Value Decomposition (SVD) and its relationship with PCA?

How do you determine the number of principal components to use?

What is meant by ‘loading’ in the context of PCA?

Explain the process of eigenvalue decomposition in PCA.

Discuss the importance of the trace of a matrix in the context of PCA.

What are the limitations of PCA when it comes to handling non-linear relationships?

Provide examples of how PCA can be used in image processing.

Explain the curse of dimensionality and how PCA can help to mitigate it.

How does PCA handle missing values in the data?

Discuss the application of PCA in feature engineering.

What is the difference between PCA and t-SNE for dimensionality reduction?

Explain how PCA can be used as a noise reduction technique.

Describe how you would apply PCA for visualization purposes.

Discuss how PCA can suffer from outlier sensitivity and ways to address it.

Can PCA be applied to categorical data? Why or why not?

What are the advantages and drawbacks of kernel PCA compared to linear PCA?

Write a Python function to perform PCA from scratch using NumPy.

Use scikit-learn to apply PCA on a high-dimensional dataset and interpret the results.

Code a Python script to visualize the eigenfaces from a given set of facial images dataset using PCA.

Implement PCA for feature extraction before applying a machine learning model.

Implement PCA in TensorFlow or PyTorch and compare the results with scikit-learn’s implementation.

Demonstrate how to choose an optimal number of dimensions with PCA in Python using the “elbow method”.

Write a Python script to automatically remove outliers before performing PCA.

Create a synthetic dataset and show the effect of PCA on classification accuracy using a machine learning algorithm before and after PCA.

How would you use PCA for data compression in a real-time streaming application?

Explain how you would apply PCA in a stock market data analysis situation.

Describe a scenario where using PCA might be detrimental to the performance of a machine learning model.

How would you decide whether to use PCA or a classification algorithm for a given dataset?

Discuss a case where PCA helped improve model performance by reducing overfitting.

Give an example of how PCA might be incorrectly applied to a dataset and propose a solution.

Discuss how you would ensure the robustness of PCA results against variations in the dataset.

How can PCA be parallelized to handle very large datasets?

What are the best practices in visualizing the components obtained from PCA?

Compare the use of PCA to select features with other feature selection methods.

Explain how Incremental PCA differs from standard PCA and when you would use it.

Discuss how Randomized PCA is used and its benefits over traditional PCA.

What recent advancements have been made concerning PCA for big data?

Discuss how robust PCA attempts to handle outliers and its practical implications.

How does Generalized PCA differ from standard PCA and what are its applications?

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