New method unlocks efficient data compression with minimal loss in quality.
The CUR decomposition method aims to approximate a matrix with low error using only a few columns. It is similar to sparse PCA methods but uses a randomized algorithm instead of convex optimization. The researchers found that CUR optimizes a sparse regression objective and has a unique sparsity structure. They also developed a sparse PCA method that achieves similar sparsity to CUR.
