In what scenarios would you use PCA, and when would you opt for other methods like LDA or t-SNE?
- Use PCA for high-dimensional data, LDA for linearly separable, t-SNE for non-linear
- Use PCA for labeled data, LDA for unlabeled, t-SNE for large-scale
- Use PCA for large-scale, LDA for visualization, t-SNE for labeled data
- Use PCA for noisy data, LDA for small-scale, t-SNE for visualizations
Use PCA when dealing with high-dimensional data and the primary goal is to reduce dimensions by maximizing variance. LDA is suitable when class labels are available, and the data is linearly separable. t-SNE is often used for non-linear data and is especially useful for visualizations, as it preserves local structures.
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