What are the differences between t-SNE and PCA?

What are the differences between t-SNE and PCA?

t-SNE (t-distributed Stochastic Neighbor Embedding) is a non-linear dimensionality reduction technique, while PCA (Principal Component Analysis) is a linear method. t-SNE is better for visualizing high-dimensional data in lower dimensions, especially for clustering, while PCA is useful for reducing dimensions while preserving variance.

When should I use t-SNE over PCA?

You should use t-SNE when you want to visualize complex data structures and clusters, especially when the data is non-linear. PCA is preferable when you need to reduce dimensions while retaining as much variance as possible.

Are there any drawbacks to using t-SNE?

Yes, t-SNE can be computationally intensive and may not preserve global structures well. It also requires careful tuning of parameters like perplexity.

Can you give an example of when to use PCA?

PCA is often used in image compression, where you want to reduce the number of pixels while retaining the essential features of the image.