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Fundamenta Mathematicae, 2000 Summary: Three sets occurring in functional analysis are shown to be of class PCA (also called \(\Sigma_2^1)\) and to be exactly of that class. The definition of each set is close to the usual objects of modern analysis, but some subtlety causes the sets to have a greater complexity than expected.
Robert Kaufman
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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.
Norma Bargary +7 more
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Tangent Phylogenetic PCA [PDF]
, 2023 Phylogenetic PCA (p-PCA) is a version of PCA for observations that are leaf nodes of a phylogenetic tree. P-PCA accounts for the fact that such observations are not independent, due to shared evolutionary history. The method works on Euclidean data, but in evolutionary biology there is a need for applying it to data on manifolds, particularly shapes ...
Akhøj, Morten +2 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
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Arab Journal of Forensic Sciences & Forensic Medicine, 2023 In the ambit of Forensic examination of the questioned documents, writing instruments often serve as an essential tool in disclosing the legitimacy of a document.
Pawan Gupta +3 more
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Statistics & Risk Modeling, 2012 Abstract Many statistical estimation techniques for high-dimensional or functional data are based on a preliminary dimension reduction step, which consists in projecting the sample X 1,...,X n onto the first D eigenvectors of the Principal Component Analysis ...
Biau, Gérard, Mas, André
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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
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Hybrid modeling and prediction of oyster norovirus outbreaks
Journal of Water and Health, 2021 This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite ...
Shima Shamkhali Chenar, Zhiqiang Deng
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Geodesic PCA in the Wasserstein space by convex PCA [PDF]
Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2017 We introduce the method of Geodesic Principal Component Analysis (GPCA) on the space of probability measures on the line, with finite second moment, endowed with the Wasserstein metric. We discuss the advantages of this approach, over a standard functional PCA of probability densities in the Hilbert space of square-integrable functions.
Jérémie Bigot +4 more
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Principal component analysis of a canning determinate tomato collection in the IPGR, Sadovo - Bulgaria [PDF]
Agroznanje, 2012 The success of a tomato breeding programme largely depends on the study of initial material and symptoms studied as well as manifestations of dependence between them. The study was conducted during the period 2008-2011 in the IPGR, Bulgaria.
Krasteva Liliya +2 more
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