Description
IE531: Algorithms for Data Analytics
For this assignment you are going to implement Edo Liberty’s Matrix Sketching algorithm [1]. Although this procedure was inspired by the family of MisraGries Streaming Algorithms, you do not have to implement it as such. That is,
a non-streaming version should be fine.
You should take a look at Dr. Liberty’s talk (click here) at the Simons Institute to get at a possible implementation-strategy for this assignment. Essentially we have a (large) data-matrix A ∈ Rm×n, where n m1
. The NEWMAT
library has a robust and fast-implementation of the SVD-Decomposition algorithm – SVD(A, D, U, V); – where A = U × D × V.t(). As you can gather from
the on line reference, NEWMAT assumes A is an m × n matrix where m ≥ n.
If n > m, then you have to work with A.t(), and make appropriate changes to
U and V to make things work.
I have provided a hint in hint.cpp on Compass. Keep in mind that I tend
to have my datasets appear in columnar-format. That said, you have to figure
out the implementation details yourself. I have also provided two datasets on
Compass. Sample outputs (using these two datasets) are shown in figure 1.
You have to take a measured-and-slow approach to getting this assignment
done. The first-part involves understanding Dr. Liberty’s algorithm. Following
this, you have to think about a slightly-different implementation (from what
is in his talk) – and this has to do with zeroing-out just the lowest-singular
value (as opposed to everything below the median-singular-value). Then, there
is the issue with getting NEWMAT’s SVD to do the needful. In the midst of all
this, there are routine-things like computing the Frobenius Norm of a matrix
(which BTW, can also be computed using its Singular Values). As you can
see from the Frobenius Norms of the Original- and Sketch-Matrices in figure 1,
my implementation comfortably meets the -specification (i.e. the reduction is
Frobenius-Norm is not much at all).
References
[1] Edo Liberty. Simple and deterministic matrix sketching. In Proceedings of
the 19th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, KDD ’13, pages 581–588, New York, NY, USA, 2013.
ACM.
1At least, as I like to think about it. Keep in mind that some authors may have more rows
in the data-matrix than columns (i.e. m n), you need to get used to these things! Nothing
that “transposing of matrices” cannot fix! Liberty’s paper uses the row-version, while his talk
uses the column-version of his algorithm.
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Figure 1: An illustration of the command-line variables.
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