Robust Fuzzy Principal Component Analysis (FPCA). A Comparative Study Concerning Interaction of Carbon−Hydrogen Bonds with Molybdenum−Oxo Bonds
Abstract
Principal component analysis (PCA) is a favorite tool in chemometrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approaches to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of outliers. In this paper we discuss a robust fuzzy PCA algorithm (FPCA). The new algorithm is illustrated on a data set concerning interaction of carbon-hydrogen bonds with transition metal-oxo bonds in molybdenum complexes. Considering, for example, a two component model, FPCA accounts for 97.20% of the total variance and PCA accounts only for 69.75%.
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When most of the data variance can be explained by the first few principal components, they can act as a useful reduced feature set.
“When most of the data variance can be explained by the first few PCs, they can act as a useful reduced feature set (54,66,67).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [66]
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- Authors
- Cundari, T. R., Sârbu, C., & Pop, H. F.
- Affiliations
- Babeș-Bolyai University (RO)
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2002-09-19
- Volume
- 42
- Issue
- 6
- Pages
- 1363-1369
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W2196884007
- Cited by
- 53
- References
- 21
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 1 · 2021: 2 · 2019: 2 · 2018: 1 · 2017: 3 · 2015: 4 · 2014: 2 · 2013: 3 · 2012: 2
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Computer Sciences (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- fuzzy principal component analysis, robust principal component analysis, fuzzy data, outliers, molybdenum oxo complexes, data transformation, chemometrics
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