2 docs tagged with "dimensionality-reduction"

t-SNE and UMAP produce the 2-D scatter plots of high-dimensional embeddings that appear everywhere — clusters of colourful dots, each supposedly a meaningful group. They are genuinely useful, and also genuinely easy to misread: knowing exactly what these plots do and do not preserve is the difference between a real insight and a confident misinterpretation.

Principal component analysis answers a simple question with surprisingly deep machinery: which few directions in a high-dimensional dataset capture most of what's actually going on? The answer — rotate onto the axes of greatest variance — turns out to be one of the most reused mathematical results in all of machine learning.