Streaming algorithms for matrix approximation

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Title Streaming algorithms for matrix approximation
Publication Type thesis
School or College College of Engineering
Department Computing
Author Desai, Amey
Date 2014-12
Description Matrices are ubiquitous in data analysis. Most real-world datasets are formulated as n x d matrix A, where n represents the number of data points and d represents the number of features. In addition, matrices are also used to store pairwise similarities between data points. Performing any large-scale machine-learning task in an efficient manner needs large matrices to be stored in memory, while also having them distributed across various machines. Since most datasets have a lower intrinsic dimension for the actual signal, a smaller sketch matrix approximates the original matrix in addition to giving a low-rank matrix, which reduces the memory requirements of machine-learning tasks. Computing low-rank matrices for best approximation accuracy is done via Singular Value Decompostion (SVD), which is computationally expensive and is not suitable in distributed environments. In this thesis, we survey various algorithms for matrix approximation and present improvements over existing work.
Type Text
Publisher University of Utah
Subject Algorithmas; Fregquent; Directions; Matrix appoximation; Random projections; Sampling; Streaming
Dissertation Institution University of Utah
Dissertation Name Master of Science
Language eng
Rights Management Copyright © Amey Desai 2014
Format application/pdf
Format Medium application/pdf
Format Extent 2,562,710 bytes
Identifier etd3/id/3327
ARK ark:/87278/s6vf06qn
Setname ir_etd
ID 196892
Reference URL https://collections.lib.utah.edu/ark:/87278/s6vf06qn
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