Deterministic graph spectral sparsification

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URI
http://hdl.handle.net/20.500.14066/3728
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Author(s)
Mendoza Granada, Fabricio Augusto; Cáceres Mercado, Sergio; Villagra, MarcosCONACYT Authority
Date of publishing
2018
Type of publication
conference paper
Abstract
An important technique in data analysis is principal component analysis or PCA. Given a covariance matrix S, in PCA we need to compute the eigenvector associated to a greatest eigenvalue of S in order to determine the direction of the so-called principal components.
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