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What is t-Distributed Stochastic Neighbor Embedding (t-SNE) and how does it differ from other dimensionality reduction techniques like PCA? Explain the core principles behind t-SNE, including its focus on preserving local structures and visualizing high-dimensional data in lower-dimensional spaces. Discuss how t-SNE computes similarity between data points and how it deals with the curse of dimensionality. Additionally, elaborate on scenarios or types of datasets where t-SNE is particularly effective for visualization and understanding complex relationships among data points, and any considerations or limitations one should be aware of when using t-SNE for analysis.

machine learning
Intermediate Level

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a non-linear dimensionality reduction technique primarily used for visualizing high-dimensional data in lower dimensions, often in 2D or 3D spaces. It differs from techniques like Principal Component Analysis (PCA) in several...

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