An incremental data-stream sketch using sparse random projections.

Aditya Krishna Menon, Gia Vinh Anh Pham, Sanjay Chawla, Anastasios Viglas

Abstract: We propose the use of random projections with a sparse matrix to maintain a sketch of a collection of high-dimensional data-streams that are updated asynchronously. This sketch allows us to estimate L2 (Euclidean) distances and dot-products with high accuracy. We verify the validity of this sketch by applying it to an online clustering problem, where we compare our results to the offline algorithm and an existing L2 sketch, and observe comparable results in terms of accuracy, and a reduced runtime cost.