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Dimensionality Reduction Using PCA and CUR Algorithm for Data on COVID-19 Tests

Abstract:

In this paper we present the results of two well known analyses, Principal Component Analysis and CUR algorithm, conducted on data related to tests of coronavirus, which were performed from May 17th to June 26th, 2020 in Ibarra, Ecuador. We analyzed the effectiveness of CUR over PCA and found out that, for our data matrix, CUR is more effective than PCA whenever the control parameters of the CUR algorithm c and k are equal. Furthermore, the results of CUR algorithm suggest that the laboratory tests D-dimer, ferritin and PCR are the most important variables.