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Lazy stochastic principal component analysis
, 2017 Stochastic principal component analysis (SPCA) has become a popular dimensionality reduction strategy for large, high-dimensional datasets. We derive a simplified algorithm, called Lazy SPCA, which has reduced computational complexity and is better ...
Li, Li +3 more
core +1 more source
Sparse Principal Component Analysis via Variable Projection [PDF]
SIAM Journal on Applied Mathematics, 2018 Sparse principal component analysis (SPCA) has emerged as a powerful technique for data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating between distinct time ...
N. Benjamin Erichson +5 more
semanticscholar +1 more source
Principal Component Analysis in ECG Signal Processing
EURASIP Journal on Advances in Signal Processing, 2007 This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loève transform is explained.
Roig José Millet +4 more
doaj +2 more sources
Modelling of Earphone Design Using Principal Component Analysis
Applied Sciences, 2023 This research investigated a mathematical model of earphone design with principal component analysis. Along with simplifying the design problem, a predictive model for the sound quality indicators, namely, total harmonic distortion, power of output ...
Lucas Kwai Hong Lui, C. K. M. Lee
doaj +1 more source
Dynamic principal component regression: Application to age-specific mortality forecasting [PDF]
ASTIN Bulletin: The Journal of the IAA (2019), 2019 In areas of application, including actuarial science and demography, it is increasingly common to consider a time series of curves; an example of this is age-specific mortality rates observed over a period of years. Given that age can be treated as a discrete or continuous variable, a dimension reduction technique, such as principal component analysis,
arxiv +1 more source
Multilevel functional principal component analysis
, 2009 The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at ...
Caffo, Brian S. +3 more
core +2 more sources
Automatic Image Alignment Using Principal Component Analysis
IEEE Access, 2018 We present an automatic technique for image alignment using a principal component analysis (PCA) that broadly consists of two steps. The first step is the segmentation of the region of interest by thresholding.
Hafiz Zia Ur Rehman, Sungon Lee
doaj +1 more source
Covariance Matrix Preparation for Quantum Principal Component Analysis
PRX Quantum, 2022 Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset.
Max Hunter Gordon +3 more
doaj +1 more source
Data Exploration Using Tableau and Principal Component Analysis
JOIV: International Journal on Informatics Visualization, 2022 This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling.
Hanna Arini Parhusip +4 more
doaj +1 more source
Principal components of nuclear mass models [PDF]
Sci. China Phys. Mech. Astron. 67, 272011 (2024)The principal component analysis approach is employed to extract the principal components contained in nuclear mass models for the first time. The effects coming from different nuclear mass models are reintegrated and reorganized in the extracted principal components.
arxiv +1 more source