# Dynamic Matrix Approximations for Data-Streaming Applications

## 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.
## References

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