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The Hypno-PC: uncovering sleep dynamics through principal component analysis and hidden Markov modeling of electrophysiological signals. [PDF]
SleepGuendelman M, Shriki O.
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Analysis of influencing factors of AIDS epidemic in Kunming based on PCA-GWR method. [PDF]
Front Public HealthZheng L +8 more
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Principal Component Analysis for any type Sequences (PCA-Seq)
Симпозиум «Математическое моделирование и высокопроизводительные вычисления в биоинформатике, биомедицине и биотехнологии», 2018 openaire +1 more source
Retraction Notice: Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model. [PDF]
J Appl Clin Med Phys, 2015 europepmc +1 more source
Immunometabolic transcriptomic signatures of diabetes-related genes predict kidney transplant rejection. [PDF]
Ren FailCasale R +4 more
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The use of principal component analysis (PCA) to characterize beef
Meat Science, 2000Principal component analysis was performed to study the relationships between chemical, physical and sensory variables (n=18) measured on longissimus thoracis et lumborum of 79 young bulls from the following ethnic groups: hypertrophied Piemontese, normal Piemontese, Friesian, crossbred hypertrophied Piemontese×Friesian, Belgian Blue and White.
DESTEFANIS, Gianluigi +3 more
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Principal Component Analysis (PCA)
Building Machine Learning and Deep Learning Models on Google Cloud Platform, 2019Principal component analysis (PCA) is an essential algorithm in machine learning. It is a mathematical method for evaluating the principal components of a dataset. The principal components are a set of vectors in high-dimensional space that capture the variance (i.e., spread) or variability of the feature space.
S. Raschka
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