Randomized algorithms for matrices and massive data sets

Authors:
Petros Drineas;Michael W. Mahoney
Affiliations:
Dept. of Computer Science, Rensselaer Polytechnic Institute, Troy, NY;Yahoo Research Labs, Sunnyvale, CA
Venue:
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Year:
2006

Citing 4
Cited 0

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Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition

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Tensor-CUR decompositions for tensor-based data

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Abstract

The tutorial will cover randomized sampling algorithms that extract structure from very large data sets modeled as matrices or tensors. Both provable algorithmic results and recent work on applying these methods to large biological and internet data sets will be discussed.