Results 21 to 30 of about 17,164 (153)
Looking for Synergies in Healthy Upper Limb Motion: A Focus on the Wrist
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023 Recent studies on human upper limb motion highlighted the benefit of dimensionality reduction techniques to extrapolate informative joint patterns.
F. Masiero +5 more
doaj +1 more source
Tracking the New Zealand English NEAR/SQUARE Merger Using Functional Principal Components Analysis
Interspeech, 2019 Copyright © 2019 ISCA The focus of the study is the application of functional principal components analysis (FPCA) to a sound change in progress in which the SQUARE and NEAR falling diphthongs are merging in New Zealand English.
M. Gubian +4 more
semanticscholar +1 more source
Journal of Medical Internet Research, 2021 BackgroundAnalyzing actigraphy data using standard circadian parametric models and aggregated nonparametric indices may obscure temporal information that may be a hallmark of the circadian impairment in psychiatric disorders.
Difrancesco, Sonia +9 more
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Functional principal component analysis (FPCA) on remote sensing data and longitudinal studies
, 2021 This dissertation consists of three main pieces of my Ph.D. dissertation, which shapes my research on multivariate functional data, change-point detection, asynchronous longitudinal data, and remote sensing data applications. The first project is motivated by NASA's first dedicated \ce{CO2} monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2)
openaire +4 more sources
Integrating Data Transformation in Principal Components Analysis [PDF]
, 2015 Principal component analysis (PCA) is a popular dimension-reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets.
Hu, Jianhua +2 more
core +3 more sources
Econometrics, 2019 The functional principal components analysis joins the advantages of the principal components analysis and provide analysis of dynamic data. The main difference in both methods is the type of data the PCA is based on multivariate data, whereas the FPCA ...
Mirosława Sztemberg-Lewandowska
semanticscholar +1 more source
Functional factor analysis for periodic remote sensing data [PDF]
, 2012 We present a new approach to factor rotation for functional data. This is achieved by rotating the functional principal components toward a predefined space of periodic functions designed to decompose the total variation into components that are nearly ...
Friedl, Mark +3 more
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COVARIATE ADJUSTED FUNCTIONAL PRINCIPAL COMPONENTS ANALYSIS FOR LONGITUDINAL DATA [PDF]
, 2010 Classical multivariate principal component analysis has been extended to functional data and termed functional principal component analysis (FPCA). Most existing FPCA approaches do not accommodate covariate information, and it is the goal of this paper
Ci-Ren Jiang, Jane-ling Wang
semanticscholar +1 more source
Recognition and Characterization of Forest Plant Communities through Remote-Sensing NDVI Time Series
Diversity, 2020 Phytosociology is a reference method to classify vegetation that relies on field data. Its classification in hierarchical vegetation units, from plant associations to class level, hierarchically reflects the floristic similarity between different sites ...
Simone Pesaresi +2 more
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On the prediction of stationary functional time series [PDF]
, 2014 This paper addresses the prediction of stationary functional time series. Existing contributions to this problem have largely focused on the special case of first-order functional autoregressive processes because of their technical tractability and the ...
Aue, Alexander +2 more
core +3 more sources