Matrix sampling algorithms for topics in machine learning

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Authors
Paul, Saurabh
Issue Date
2015-05
Type
Electronic thesis
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Language
ENG
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Computer science
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Abstract
4. Adaptive Sampling algorithm for matrix reconstruction: We introduce a new adaptive sampling algorithm for computing low-rank matrix approximation. We are given a matrix A and a target rank k. The algorithm runs in t iterations and selects a subset of columns of the matrix. It computes a rank-k approximation to the matrix that is as good as the best rank-k approximation that would have been obtained by using all the columns.
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May 2015
School of Science
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Rensselaer Polytechnic Institute, Troy, NY
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