What is 'Dimensionality Reduction' via Random Projections?

What is 'Dimensionality Reduction' via Random Projections?

Approach: Describe projecting high-dimensional data into lower dimensions while preserving distances.

A technique based on the Johnson-Lindenstrauss lemma, which states that high-dimensional data can be projected into a lower-dimensional space while nearly preserving the distances between points using a random matrix.

Follow-up: How does it compare to PCA in terms of computation?

Key concepts: johnson-lindenstrauss lemma, euclidean distance, sparsity.