UMAP
UMAP, short for Uniform Manifold Approximation and Projection for Dimension Reduction, is the dimension reduction technique behind pix-plot. It enables high-dimensional data, such as a set of thousands of images, to be placed on two or three dimension (2d or 3d space) and put in relation to each other by overall and local clustering.
UMAP is a fast and effective technique for visualizing high-dimensional datasets, outperforming t-SNE in speed and global structure preservation. Understanding UMAP’s parameters, like n_neighbors and min_dist, is crucial for controlling the balance between local and global structure in the final projection. Despite its advantages, interpreting UMAP results requires careful consideration of hyperparameters and an awareness of the algorithm’s limitations.