Following up on this morning entry on a better embedding for least squares and SVD, here is a result for eigenvalues: Evidently all these entries are listed under the RandNLA tag: I can see a whole bunch of application for something like this. Here it is: Eigenvalues of a matrix in the streaming model by Alexandr Andoni and Huy L. Nguyen. The abstract reads:

We study the question of estimating the eigenvalues of a matrix in the streaming model, addressing a question posed in [Mut05]. We show that the eigenvalue \heavy hitters" of a matrix can be computed in a single pass. In particular, we show that the -heavy hitters (in the `1 or `2 norms) can be estimated in space proportional to 1= 2. Such a dependence on is optimal. We also show how the same techniques may give an estimate of the residual error tail of a rank-k approximation of the matrix (in the Frobenius norm), in space proportional to k 2 All our algorithms are linear and hence can support arbitrary updates to the matrix in the stream. In fact, what we show can be seen as a form of a bi-linear dimensionality reduction: if we multiply an input matrix with projection matrices on both sides, the resulting matrix preserves the top eigenvalues and the residual Frobenius norm.

Image Credit: NASA/JPL/Space Science Institute,

W00076689.jpg was taken on November 06, 2012 and received on Earth November 06, 2012. The camera was pointing toward SATURN-RINGS at approximately 1,292,973 miles (2,080,838 kilometers) away, and the image was taken using the CL1 and CL2 filters.

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