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Related Questions
- What are the potential issues with PCA when dealing with high-dimensional data and how can they be addressed?
- How does the curse of dimensionality affect the performance of PCA in high-dimensional spaces?
- What are some alternative dimensionality reduction techniques that can handle high-dimensional feature spaces more effectively?
- Can PCA be used for feature selection, and if so, what are the limitations of using PCA for feature selection in high-dimensional spaces?
- How does the choice of hyperparameters in PCA impact its performance in high-dimensional spaces?
- What are the implications of using PCA on high-dimensional data with a large number of irrelevant features?
- Are there any specific scenarios or applications where PCA is not suitable for high-dimensional data, and what are the alternatives in such cases?
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