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Related Questions
- What are the primary goals and limitations of PCA in dimensionality reduction?
- How does t-SNE differ from PCA in terms of preserving global and local structure in data?
- In what scenarios would you prefer to use autoencoders over PCA for dimensionality reduction?
- Can PCA be used for non-linear dimensionality reduction, and if not, what alternatives are available?
- How do the reconstruction errors obtained from autoencoders relate to the quality of the dimensionality reduction?
- What is the role of hyperparameter tuning in achieving optimal results with PCA and other dimensionality reduction techniques?
- Can PCA be used for feature selection, and if so, what are the advantages and disadvantages of this approach?
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