We are concerned with rendering large document collections into human digestible form. Clustering has been successfully used as a tool for this.
Most clustering algorithms are of a combinatorial flavor.
Traditional SVD algorithms are too time consuming to be used here. We describe a new randomized Monte Carlo algorithm for SVD where by doing Linear Algebra on a random sub-matrix of a matrix, one can infer an approximate SVD of the whole matrix. The system is fast enough to be used as a web search tool. Some thoughts on using the randomized Linear Algebra algorithm for Image Compression and other applications will also be described. This event is from the MIT EECS 1999-00 archive .