Dynamic Matrix Approximations for Data-Streaming Applications

Adam Kirsch
Mentors: Graham Cormode, S. Muthu Muthukrishnan

Project Summary

Many information retrieval techniques rely on analyzing the spectral structure of a large matrix. Unfortunately, the matrix of interest is often so large that it cannot be stored in memory, so progressive algorithms that make few passes over the data become desirable. In fact, it is conceivable that the given matrix is so large that it cannot even be stored on disk, and that it is presented to us in the form of increments and decrements to the coefficients from multiple sources. This is the data streaming scenario. We are looking for small-space data structures that provide good approximations for the spectral structure of a matrix in this setting. So far, our techniques are based on results from the study of random matrices. If you like, you can take a look at some slides.


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G. Cormode and S. Muthukrishnan, Improved Data Stream Summaries: The Count-Min Sketch and its Applications, DIMACS Tech. Report 2003-20, 2003.

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