Positive matrix factorization of large real-time atmospheric mass spectrometry datasets using error-weighted randomized hierarchical alternating least squares Article Swipe
Benjamin Sapper
,
S. W. Youn
,
Daven K. Henze
,
Manjula R. Canagaratna
,
Harald Stark
,
J. L. Jiménez
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/gmd-18-2891-2025
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/gmd-18-2891-2025
Weighted positive matrix factorization (PMF) has been used by scientists to find small sets of underlying factors in environmental data. However, as the size of the data has grown, increasing computational costs have made it impractical to use traditional methods for this factorization. In this paper, we present a new external weighting method to dramatically decrease computational costs for these traditional algorithms. The external weighting scheme, along with the randomized hierarchical alternating least squares (RHALS) algorithm, was applied to the Southern Oxidant and Aerosol Study (SOAS 2013) dataset of gaseous highly oxidized multifunctional molecules (HOMs). The modified RHALS algorithm successfully reproduced six previously identified interpretable factors, with the total computation time of the nonoptimized code showing potential improvements of the order of 1 to 2 orders of magnitude compared to competing algorithms. We also investigate rotational ambiguity in the solution and present a simple “pulling” method to rotate a set of factors. This method is shown to find alternative solutions and, in some cases, lower the weighted residual error of the algorithm.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/gmd-18-2891-2025
- https://gmd.copernicus.org/articles/18/2891/2025/gmd-18-2891-2025.pdf
- OA Status
- gold
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410508735
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- OpenAlex ID
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https://openalex.org/W4410508735Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/gmd-18-2891-2025Digital Object Identifier
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Positive matrix factorization of large real-time atmospheric mass spectrometry datasets using error-weighted randomized hierarchical alternating least squaresWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-05-19Full publication date if available
- Authors
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Benjamin Sapper, S. W. Youn, Daven K. Henze, Manjula R. Canagaratna, Harald Stark, J. L. JiménezList of authors in order
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https://doi.org/10.5194/gmd-18-2891-2025Publisher landing page
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https://gmd.copernicus.org/articles/18/2891/2025/gmd-18-2891-2025.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://gmd.copernicus.org/articles/18/2891/2025/gmd-18-2891-2025.pdfDirect OA link when available
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Matrix (chemical analysis), Factorization, Mass spectrometry, Mathematics, Statistics, Computer science, Algorithm, Chemistry, ChromatographyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.paper, | 46 |
| abstract_inverted_index.rotate | 148 |
| abstract_inverted_index.simple | 144 |
| abstract_inverted_index.(HOMs). | 95 |
| abstract_inverted_index.(RHALS) | 75 |
| abstract_inverted_index.Aerosol | 84 |
| abstract_inverted_index.Oxidant | 82 |
| abstract_inverted_index.applied | 78 |
| abstract_inverted_index.dataset | 88 |
| abstract_inverted_index.factors | 17 |
| abstract_inverted_index.gaseous | 90 |
| abstract_inverted_index.methods | 40 |
| abstract_inverted_index.present | 48, 142 |
| abstract_inverted_index.scheme, | 66 |
| abstract_inverted_index.showing | 116 |
| abstract_inverted_index.squares | 74 |
| abstract_inverted_index.However, | 21 |
| abstract_inverted_index.Southern | 81 |
| abstract_inverted_index.Weighted | 1 |
| abstract_inverted_index.compared | 129 |
| abstract_inverted_index.decrease | 56 |
| abstract_inverted_index.external | 51, 64 |
| abstract_inverted_index.factors, | 106 |
| abstract_inverted_index.factors. | 152 |
| abstract_inverted_index.modified | 97 |
| abstract_inverted_index.oxidized | 92 |
| abstract_inverted_index.positive | 2 |
| abstract_inverted_index.residual | 168 |
| abstract_inverted_index.solution | 140 |
| abstract_inverted_index.weighted | 167 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.algorithm | 99 |
| abstract_inverted_index.ambiguity | 137 |
| abstract_inverted_index.competing | 131 |
| abstract_inverted_index.magnitude | 128 |
| abstract_inverted_index.molecules | 94 |
| abstract_inverted_index.potential | 117 |
| abstract_inverted_index.solutions | 160 |
| abstract_inverted_index.weighting | 52, 65 |
| abstract_inverted_index.algorithm, | 76 |
| abstract_inverted_index.algorithm. | 172 |
| abstract_inverted_index.identified | 104 |
| abstract_inverted_index.increasing | 30 |
| abstract_inverted_index.previously | 103 |
| abstract_inverted_index.randomized | 70 |
| abstract_inverted_index.reproduced | 101 |
| abstract_inverted_index.rotational | 136 |
| abstract_inverted_index.scientists | 10 |
| abstract_inverted_index.underlying | 16 |
| abstract_inverted_index.algorithms. | 62, 132 |
| abstract_inverted_index.alternating | 72 |
| abstract_inverted_index.alternative | 159 |
| abstract_inverted_index.computation | 110 |
| abstract_inverted_index.impractical | 36 |
| abstract_inverted_index.investigate | 135 |
| abstract_inverted_index.traditional | 39, 61 |
| abstract_inverted_index.dramatically | 55 |
| abstract_inverted_index.hierarchical | 71 |
| abstract_inverted_index.improvements | 118 |
| abstract_inverted_index.nonoptimized | 114 |
| abstract_inverted_index.successfully | 100 |
| abstract_inverted_index.computational | 31, 57 |
| abstract_inverted_index.environmental | 19 |
| abstract_inverted_index.factorization | 4 |
| abstract_inverted_index.interpretable | 105 |
| abstract_inverted_index.“pulling” | 145 |
| abstract_inverted_index.factorization. | 43 |
| abstract_inverted_index.multifunctional | 93 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.2375339 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |