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.
S. Marukatat
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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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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.
Jérémie Bigot +3 more
semanticscholar +7 more sources
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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A chemometric approach to the headspace sampled volatiles of selected Salvia species from Southeastern Serbia [PDF]
Botanica Serbica, 2022 Headspace sampling is a fast, simple and economical way to prepare plant samples for analysis by gas chromatography. For the first time, the composition of the head space volatiles (HSV) of six Salvia species (S. verticillata, S. glutinosa, S.
Emilija Kostić +5 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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Grey Relational Analysis and Principal Component Analysis based optimization of process parameters in turning of EN-8 Steel [PDF]
INCAS Bulletin, 2022 The present work investigates the optimum machining parameters while turning EN 8 steel by employing hybrid Grey relational analysis (GRA) and principal component analysis (PCA) techniques. Experiments were designed based on the central composite design (
CH. LAKSHMI SRINIVAS +3 more
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Development of the Instruments for measuring the quality of E-Banking services in the Republic of Serbia: E-BSrb-QUAL [PDF]
Bankarstvo, 2022 The most commonly used model to measure the quality of electronic services is the E-Service Quality - E-SQ (E-S-QUAL and E-RecS-QUAL). Acknowledging the results of existing research and the attempts to create a unique model for measuring the quality of e-
Đorđević Bojan S.
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HPTLC-based metabolomics for the investigation of metabolic changes during plant development: The case study of Artemisia annua [PDF]
Journal of the Serbian Chemical Society, 2022 The application of high performance thin layer chromatography (HPTLC)-based non-targeted metabolomics as a holistic approach to compare fingerprints of metabolite changes during Artemisia annua development is described.
Stanković-Jeremić Jovana +6 more
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