What are some common dimension reduction techniques in machine learning?

What are some common dimension reduction techniques in machine learning?

Some common dimension reduction techniques include PCA (Principal Component Analysis), t-SNE, and LDA (Linear Discriminant Analysis). Would you like to know more about any specific technique?

Can you explain how PCA works?

PCA works by transforming the data into a new coordinate system, where the greatest variance comes first. It reduces dimensions while preserving as much variance as possible.

What are the advantages of using t-SNE?

t-SNE is great for visualizing high-dimensional data in lower dimensions, especially for clustering. It preserves local structures well.

Are there any drawbacks to using LDA?

Yes, LDA assumes that the data follows a Gaussian distribution and may not perform well if this assumption is violated.