Results 31 to 40 of about 564,038 (269)
Approximation bounds for sparse principal component analysis [PDF]
Mathematical Programming, 2014 We produce approximation bounds on a semidefinite programming relaxation for sparse principal component analysis. These bounds control approximation ratios for tractable statistics in hypothesis testing problems where data points are sampled from Gaussian models with a single sparse leading component.
d'Aspremont, Alexandre +2 more
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Large-scale paralleled sparse principal component analysis [PDF]
Multimedia Tools and Applications, 2014 Principal component analysis (PCA) is a statistical technique commonly used in multivariate data analysis. However, PCA can be difficult to interpret and explain since the principal components (PCs) are linear combinations of the original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and interpretability by approximating sparse PCs ...
Liu, W. +4 more
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Eigenvectors from Eigenvalues Sparse Principal Component Analysis [PDF]
Journal of Computational and Graphical Statistics, 2021 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.
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Advances in Mechanical Engineering, 2017 In this article, for the first time, the optical flow and principal component analysis followed independent component analysis are combined for monitoring the motion process of robotic-arm-based system.
Song Fan +3 more
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Multilevel sparse functional principal component analysis
Stat, 2014 We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis was proposed recently for such data when functions are ...
Di, Chongzhi +2 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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Principal Component Analysis With Sparse Fused Loadings [PDF]
Journal of Computational and Graphical Statistics, 2010 In this article, we propose a new method for principal component analysis (PCA), whose main objective is to capture natural "blocking" structures in the variables. Further, the method, beyond selecting different variables for different components, also encourages the loadings of highly correlated variables to have the same magnitude. These two features
Jian, Guo +4 more
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Cutting Plane Generation through Sparse Principal Component Analysis
SIAM Journal on Optimization, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Santanu S. Dey +3 more
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Evaluating carbon neutrality potential in China based on sparse principal component analysis
Energy Reports, 2023 The resource endowments, development levels and emission reduction potential of different provinces in China are different. Accurately judging regional differences of carbon neutrality potential of each province is helpful to analyze the low-carbon ...
YuKun Liu, Xiyan Li, Xiqiao Lin
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Alternating direction method of multipliers for penalized zero-variance discriminant analysis [PDF]
, 2015 We consider the task of classification in the high dimensional setting where the number of features of the given data is significantly greater than the number of observations.
Ames, Brendan +2 more
core +3 more sources