Intensive Variability Extraction Article Swipe
Related Concepts
Principal component analysis
Extraction (chemistry)
Spatial analysis
Statistics
Mathematics
Computer science
Data mining
Pattern recognition (psychology)
Artificial intelligence
Chemistry
Chromatography
Tsubasa Kohyama
,
Hiroaki Miura
,
Shoichiro Kido
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.2151/sola.2021-043
· OA: W3212350966
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.2151/sola.2021-043
· OA: W3212350966
A modified version of the principal component analysis (PCA) is introduced by reconsidering statistical degrees of freedom in spatial dimensions based on spatial auto-correlations. In the conventional PCA, data points that represent equal areas are assumed to have equal amount of information. In our new method, the intensive variability extraction (IVE), data points correlated with less other data points are weighted more before performing PCA. Hence, variability with independent information is emphasized, even if the variability is confined to small areas.
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