Results 31 to 40 of about 232,203 (264)
Meta Sparse Principal Component Analysis
, 2022 29 pages, 7 ...
Banerjee, Imon, Honorio, Jean
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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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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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Sparse Exploratory Factor Analysis [PDF]
, 2017 Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables.
A Edelman +21 more
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Structured Sparse Principal Component Analysis
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2009 We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This \emph{structured sparse PCA} is based on a structured regularization recently introduced by [1].
R. Jenatton, G. Obozinski, F. Bach
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International Journal of Concrete Structures and Materials, 2019 While internal and external unbonded tendons are widely utilized in concrete structures, an analytical solution for the increase in unbonded tendon stress at ultimate strength, $$\Delta f_{ps}$$ Δfps , is challenging due to the lack of bond between ...
Eric McKinney +3 more
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Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis
Measurement Science Review, 2018 This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion.
Andráš Imrich +3 more
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Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices [PDF]
, 2015 This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection.
Cai, Tony, Ma, Zongming, Wu, Yihong
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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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Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring
IEEE Access, 2019 Kernel principal component analysis (KPCA) has been widely used for nonlinear process monitoring. However, since the principal components are linear combinations of all kernel functions, traditional KPCA suffers from poor interpretation and high ...
Lingling Guo +3 more
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