What is t-SNE in machine learning?

What is t-SNE in machine learning?

t-SNE, or t-distributed Stochastic Neighbor Embedding, is a dimensionality reduction technique used for visualizing high-dimensional data.

How does t-SNE differ from PCA?

While PCA (Principal Component Analysis) focuses on preserving global structures, t-SNE emphasizes local structures, making it better for visualizing clusters.

Can you give an example of when to use t-SNE?

t-SNE is particularly useful when you want to visualize complex datasets, like images or gene expression data, in a 2D or 3D space.

Are there any limitations to using t-SNE?

Yes, t-SNE can be computationally intensive and may not preserve the global structure of the data well, which can lead to misleading interpretations.