Tutorial on PCA and approximate PCA and approximate kernel PCA
Artificial Intelligence Review, 2022 AbstractPrincipal Component Analysis (PCA) is one of the most widely used data analysis methods in machine learning and AI. This manuscript focuses on the mathematical foundation of classical PCA and its application to a small-sample-size scenario and a large dataset in a high-dimensional space scenario.
Sanparith Marukatat
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PCA of waveforms and functional PCA: A primer for biomechanics [PDF]
Journal of Biomechanics, 2021 Principal components analysis (PCA) of waveforms and functional PCA (fPCA) are statistical approaches used to explore patterns of variability in biomechanical curve data, with fPCA being an accepted statistical method grounded within the functional data analysis (FDA) statistical framework.
John Warmenhoven +2 more
exaly +3 more sources
Validation of nonlinear PCA [PDF]
Neural Processing Letters, 2012 Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity.
A Herman +26 more
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Singular Learning of Deep Multilayer Perceptrons for EEG-Based Emotion Recognition
Frontiers in Computer Science, 2021 Human emotion recognition is an important issue in human–computer interactions, and electroencephalograph (EEG) has been widely applied to emotion recognition due to its high reliability.
Weili Guo +6 more
doaj +1 more source
Melissopalynology analysis, determination of physicochemical parameters, sugars and phenolics in Maltese honey collected in different seasons [PDF]
Journal of the Serbian Chemical Society, 2022 Malta, a country renowned for its honey, has not been extensively mentioned in studies based on honey. In addition to many parameters, the collection period affects honey quality, precisely due to the different floral composition that exists during a ...
Bugeja Douglas Adrian +9 more
doaj +1 more source
Canadian Journal of Statistics, 2023 AbstractMahalanobis distance of covariate means between treatment and control groups is often adopted as a balance criterion when implementing a rerandomization strategy. However, this criterion may not work well for high‐dimensional cases because it balances all orthogonalized covariates equally.
Hengtao Zhang +2 more
openaire +3 more sources
Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence [PDF]
, 2020 Purpose of this study was to evaluate the diagnostic performance of T1 relaxation time (T1) for differentiating prostate cancer (PCa) from benign tissue as well as high- from low-grade PCa.
Baur, Alexander D. J. +7 more
core +1 more source
Bazı Tescilli Nohut Çeşitlerinin Elek Analiz Değerleri
Turkish Journal of Agriculture: Food Science and Technology, 2021 Ülkemizde nohut hem yaş hem de kuru olarak farklı süreçlerden geçerek tüketilmektedir. Tüketimde tercihen iri taneli nohutlar tercih edilirken 6 mm elek altı genellikle tercih edilmemektedir.
Hamdi Özaktan
doaj +1 more source
Elevated Tumor Lactate and Efflux in High-grade Prostate Cancer demonstrated by Hyperpolarized 13C Magnetic Resonance Spectroscopy of Prostate Tissue Slice Cultures. [PDF]
, 2020 Non-invasive assessment of the biological aggressiveness of prostate cancer (PCa) is needed for men with localized disease. Hyperpolarized (HP) 13C magnetic resonance (MR) spectroscopy is a powerful approach to image metabolism, specifically the ...
Ahamed, Fayyaz +12 more
core +1 more source
Upregulated wnt-11 and mir-21 expression trigger epithelial mesenchymal transition in aggressive prostate cancer cells [PDF]
, 2020 Prostate cancer (PCa) is the second-leading cause of cancer-related death among men. microRNAs have been identified as having potential roles in tumorigenesis.
Arisan, E.D. +15 more
core +1 more source