Stochastic convex sparse principal component analysis. [PDF]
EURASIP J Bioinform Syst Biol, 2016 Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present
Baytas IM +4 more
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Craniofacial similarity analysis through sparse principal component analysis.
PLoS ONE, 2017 The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery.
Junli Zhao +7 more
doaj +4 more sources
Retraction: Craniofacial similarity analysis through sparse principal component analysis [PDF]
PLoS ONEPLOS One Editors
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Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA). [PDF]
J Comput Graph Stat, 2022 We present a novel technique for sparse principal component analysis. This method, named Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian matrix from the eigenvalues of the full matrix and associated sub-matrices.
Frost HR.
europepmc +5 more sources
Robust sparse principal component analysis. [PDF]
SSRN Electronic Journal, 2011 A method for principal component analysis is proposed that is sparse and robust at the same time. The sparsity delivers principal components that have loadings on a small number of variables, making them easier to interpret.
Croux, Christophe +2 more
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MULTILEVEL SPARSE FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS [PDF]
Stat, 2010 The basic observational unit in this paper is a function. Data are assumed to have a natural hierarchy of basic units. A simple example is when functions are recorded at multiple visits for the same subject. Di et al.
Crainiceanu, Ciprian M., Di, Chong-Zhi
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Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis [PDF]
Scientific Reports, 2022 Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error).
Mohammad Mamouei +6 more
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Incorporating biological information in sparse principal component analysis with application to genomic data [PDF]
BMC Bioinformatics, 2017 Background Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data.
Ziyi Li, Sandra E. Safo, Qi Long
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Lymphocyte–monocyte–neutrophil index: a predictor of severity of coronavirus disease 2019 patients produced by sparse principal component analysis [PDF]
Virology Journal, 2021 Background It is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, thus more effective predictors should be developed.
Yingjie Qi +5 more
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Generalized power method for sparse principal component analysis [PDF]
, 2008 In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of
Journée, Michel +3 more
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