What are some alternatives to PCA in NumPy?

What are some alternatives to PCA in NumPy?

Some alternatives to PCA include t-SNE, UMAP, and LDA. Would you like to know more about any of these?

Can you explain how t-SNE works?

t-SNE (t-distributed Stochastic Neighbor Embedding) is a technique for dimensionality reduction that is particularly well-suited for visualizing high-dimensional data. It converts similarities between data points into joint probabilities and minimizes the divergence between these probabilities in lower dimensions.

How does UMAP differ from t-SNE?

UMAP (Uniform Manifold Approximation and Projection) is generally faster than t-SNE and preserves more of the global structure of the data. It also allows for more flexibility in the number of neighbors considered during the embedding process.

Are there any specific use cases for LDA?

Yes, LDA (Linear Discriminant Analysis) is often used in supervised learning for classification tasks, especially when you want to reduce dimensionality while preserving class separability.