Results 221 to 230 of about 365,137 (249)

Multi-way functional principal components analysis

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013
Many examples of multi-way or tensor-valued data, such as in climate studies, neuroimaging, chemometrics, and hyperspectral imaging, are structured meaning that variables are associated with locations. Tensor decompositions, or higher-order principal components analysis (HOPCA), are a classical method for dimension reduction and pattern recognition for
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Principal Component Analysis in Transfer Function

2016
This chapter explores the transfer function in detail, with multiple linear regressions, and principal component analysis (PCA). Furthermore, it contains the slight description of various types of regression and emphasizes on the PCA and the calculations of principal components (PCs) in detail.
T. M. V. Suryanarayana, P. B. Mistry
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Principal components analysis for functional data

1997
For many reasons, principal components analysis (PCA) of functional data is a key technique to consider. First, our own experience is that, after the preliminary steps of registering and displaying the data, the user wants to explore that data to see the features characterizing typical functions.
J. O. Ramsay, B. W. Silverman
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Functional outlier detection with robust functional principal component analysis

Computational Statistics, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pallavi Sawant, Nedret Billor, Hyejin Shin   +2 more
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Robust Functional Principal Component Analysis

2014
When dealing with multivariate data robust principal component analysis (PCA), like classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator s n may be used in the maximization problem.
Juan Lucas Bali, Graciela Boente
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Interpreting the Principal Component Analysis of Multivariate Density Functions

Communications in Statistics - Theory and Methods, 2015
Functional principal component analysis (FPCA) as a reduction data technique of a finite number T of functions can be used to identify the dominant modes of variation of numeric three-way data.We carry out the FPCA on multidimensional probability density functions, relate this method to other standard methods and define its centered or standardized ...
Boumaza, Rachid, Yousfi, Smail, Demotes-Mainard, Sabine   +2 more
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Principal component analysis for functional data

2018
This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases
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A Time-Varying Distance Based Interval-Valued Functional Principal Component Analysis Method – A Case Study of Consumer Price Index

Information Sciences, 2022
Tomas Balezentis, Chonghui Zhang, Kaili Wang   +2 more
exaly  

Sparse Multivariate Functional Principal Component Analysis

Stat, 2022
Jun Song, Kyongwon Kim
exaly  

Principal Component Analysis of Hybrid Functional and Vector Data

Statistics in Medicine, 2021
Jeong Hoon Jang
exaly