Results 21 to 30 of about 50,718 (252)
Federated Learning for Sparse Principal Component Analysis
2023 IEEE International Conference on Big Data (BigData), 2023 11 pages, 7 figures, 1 table.
Sin Cheng Ciou +3 more
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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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Sparse Principal Component Analysis [PDF]
Journal of Computational and Graphical Statistics, 2006 Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results.
Hui Zou +2 more
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Face Recognition Based on Robust Principal Component Analysis and Kernel Sparse Representation [PDF]
Jisuanji gongcheng, 2016 Aiming at the problems that the existing face recognition methods are hard to efficiently overcome the effect of noise and error disturbance (such as illumination,occlusion,and face expression).Kernel sparse representation classification based on Robust ...
LIAO Ruihua,LI Yongfan,LIU Hong
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Land, 2022 The primary purpose of this study is to develop a method that can assist in exploring infrastructure-related multidimensional data. The spatial distribution of social infrastructure, including housing and service facilities, is usually uneven across a ...
Seong-Yun Hong +4 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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Sparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains [PDF]
Journal of Artificial Intelligence and Data Mining, 2020 In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important.
S. Mavaddati
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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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Certifiably optimal sparse principal component analysis [PDF]
Mathematical Programming Computation, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lauren Berk, Dimitris Bertsimas
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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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