Results 51 to 60 of about 90,827 (166)
Sparsistency and agnostic inference in sparse PCA
, 2015 The presence of a sparse "truth" has been a constant assumption in the theoretical analysis of sparse PCA and is often implicit in its methodological development.
Lei, Jing, Vu, Vincent Q.
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Sparse Kernel PCA for Outlier Detection
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018 Accepted at IEEE ICMLA 2018 for Oral ...
Rudrajit Das +2 more
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eLife, 2023 Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity.
Vasileios Karageorgiou +3 more
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An Augmented Lagrangian Approach for Sparse Principal Component Analysis [PDF]
, 2009 Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduction with numerous applications in science and engineering. However, the standard PCA suffers from the fact that the principal components (PCs) are usually
Lu, Zhaosong, Zhang, Yong
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High Dimensional Semiparametric Scale-Invariant Principal Component Analysis
, 2014 We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are ...
Han, Fang, Liu, Han
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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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Image Super-Resolution Via Wavelet Feature Extraction and Sparse Representation [PDF]
Radioengineering, 2018 This paper proposes a novel Super-Resolution (SR) technique based on wavelet feature extraction and sparse representation. First, the Low-Resolution (LR) image is interpolated employing the Lanczos operation. Then, the image is decomposed into sub-bands (
V. Alvarez-Ramos +2 more
doaj
Exact and Approximation Algorithms for Sparse PCA
CoRR, 2020 49 pages, 1 ...
Yongchun Li, Weijun Xie 0001
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Penalized Orthogonal Iteration for Sparse Estimation of Generalized Eigenvalue Problem
, 2017 We propose a new algorithm for sparse estimation of eigenvectors in generalized eigenvalue problems (GEP). The GEP arises in a number of modern data-analytic situations and statistical methods, including principal component analysis (PCA), multiclass ...
Anant Agrawal (3953690) +6 more
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Sparse PCA Beyond Covariance Thresholding
CoRR, 2023 In the Wishart model for sparse PCA we are given $n$ samples $Y_1,\ldots, Y_n$ drawn independently from a $d$-dimensional Gaussian distribution $N({0, Id + βvv^\top})$, where $β> 0$ and $v\in \mathbb{R}^d$ is a $k$-sparse unit vector, and we wish to recover $v$ (up to sign).
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