Positive matrix factorization of large real-time atmospheric mass spectrometry datasets using error-weighted randomized hierarchical alternating least squares
Benjamin C. Sapper,Sean Youn,Daven K. Henze,Manjula Canagaratna,Harald Stark,and Jose L. Jimenez
Benjamin C. Sapper
Department of Applied Mathematics, University of Colorado Boulder, 11 Engineering Dr, Boulder, CO 80309, United States
Aerodyne Research, 45 Manning Road, Billerica, MA 01821, United States
Harald Stark
Center for Aerosol and Cloud Chemistry, Aerodyne Research, 45 Manning Road, Billerica, MA 01821, United States
Department of Chemistry and Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO 80309, United States
Department of Chemistry and Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO 80309, United States
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Positive matrix factorization (PMF) has been used by atmospheric scientists to extract underlying factors present in large datasets. This paper presents a new technique for error-weighted PMF that drastically reduces the computational costs of previously developed algorithms. We use this technique to deliver interpretable factors and solution diagnostics from an atmospheric chemistry dataset.
Positive matrix factorization (PMF) has been used by atmospheric scientists to extract...
