Results 1 to 10 of about 527,673 (240)
Tutorial on PCA and approximate PCA and approximate kernel PCA
Artificial Intelligence Review, 2022 Principal 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 ...
S. Marukatat
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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 +6 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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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
openaire +5 more sources
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
Principal Component Analysis (PCA)
Encyclopedia of Autism Spectrum Disorders, 2021 • Patternrecognition in high-dimensional spaces-P roblems arise when performing recognition in a high-dimensional space (e.g., curse of dimensionality).-S ignificant improvements can be achievedb yfi rst mapping the data into a lower-dimensionality space.
Takio Kurita
semanticscholar +1 more source
تعیین تیپ اقلیمی مناطق مختلف با استفاده از روش تحلیل مؤلفه های اصلی [PDF]
مدیریت آب و آبیاری, 2021 ﺗﻐﯿﯿﺮات اﻗﻠﯿﻤﯽ در اﺑﻌﺎد وﺳﯿﻊ ﺳﺒﺐ اﻓﺰاﯾﺶ تغییر شاخص‌های حدی می‌شود. از آنجا که این شاخص‌ها نقش مهمی در بروز خشک‌سالی، سیل‌ها و دیگر بلایای اقلیمی دارند، بررسی رفتار آن‌ها در بستر تغییرات اقلیمی ضروری است.
فرحناز خرم آبادی +4 more
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Scientific Reports, 2022 Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information.
E. Elhaik
semanticscholar +1 more source
IEEE Transactions on Geoscience and Remote Sensing, 2022 As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral ...
Hang Fu +4 more
semanticscholar +1 more source
National Accounting Review, 2021 There has been a monotonic increase in research investigating the performance of commercial banks across the globe. This is a recognition that the banking industry has a significant contribution to the service sector and national output.
Lloyd George Banda
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