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Related Questions
- What are the key differences between PCA and t-SNE in terms of their applications and limitations in dimensionality reduction?
- Can you explain how PCA can be used to detect and remove highly correlated features in a dataset?
- How does t-SNE handle high-dimensional data with non-linear relationships between features, and what are its advantages in this regard?
- What is the role of feature scaling in PCA and t-SNE, and how does it impact the quality of the reduced dimensionality space?
- Can PCA be used to reduce the dimensionality of text data, and if so, what are the challenges and limitations of doing so?
- How can t-SNE be used to visualize and identify clusters in high-dimensional data with non-linear relationships between features?
- What are some common applications of PCA and t-SNE in real-world machine learning tasks, such as clustering, classification, and regression?
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