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Recovering PCA and sparse PCA via hybrid-\((\ell_1,\ell_2)\) sparse sampling of data elements
J. Mach. Learn. Res., 2017Summary: This paper addresses how well we can recover a data matrix when only given a few of its elements. We present a randomized algorithm that element-wise sparsifies the data, retaining only a few of its entries. Our new algorithm independently samples the data using probabilities that depend on both squares (\(\ell_2\) sampling) and absolute ...
Abhisek Kundu +2 more
openaire +2 more sources
A Communication-Efficient and Privacy-Aware Distributed Algorithm for Sparse PCA
Computational Optimization and Applications, 2023Xin Liu
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De-Biased Sparse PCA: Inference for Eigenstructure of Large Covariance Matrices
IEEE Transactions on Information Theory, 2021Jana Janková, Sara van de Geer
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Joint Group Sparse PCA for Compressed Hyperspectral Imaging
IEEE Transactions on Image Processing, 2015Zohaib Khan, Faisal Shafait, Ajmal Mian
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Convex Sparse PCA for Unsupervised Feature Learning
ACM Transactions on Knowledge Discovery From Data, 2017Chengqi Zhang, Yi Yang, Feiping Nie
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Sparse Variable PCA Using Geodesic Steepest Descent
IEEE Transactions on Signal Processing, 2008Magnús Örn Úlfarsson +2 more
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Tensor Sparse PCA and Face Recognition: A Novel Approach
SN Applied Sciences, 2020Linh Tran, Trang Hoang, Lộc Trần
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