A Fast Random Sampling Algorithm for Sparsifying Matrices

We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more than the Chernoff-Hoeffding bounds. Despite the simplicity, the approximation is comparable and sometimes better than previous work. Our algorithm computes the sparse matrix approximation in a single pass over the data. Further, most of the entries in the output matrix are quantized, and can be succinctly represented by a bit vector, thus leading to much savings in space.

• Type: Conference
• Year: 2006
• Where: APPROX
• Authors: Sanjeev Arora, Elad Hazan, Satyen Kale
• Keywords: Algorithms, APPROX 2006, Approximation, Random-sampling Based Procedure, Sparse Matrix Approximation