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Related Questions
- What are the key differences between LLE and other manifold learning algorithms?
- How does LLE handle high-dimensional data, and what are the challenges it faces?
- Can you explain the role of nearest neighbors in LLE, and how they are used to construct the embedding?
- What are the strengths and weaknesses of LLE in terms of computational complexity?
- How does LLE perform compared to other dimensionality reduction techniques, such as PCA and t-SNE?
- What are some common applications of LLE in real-world problems, and how does it benefit the analysis?
- Can you provide a step-by-step example of how to implement LLE from scratch, including the selection of parameters?
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