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Recovering PCA and sparse PCA via hybrid-\((\ell_1,\ell_2)\) sparse sampling of data elements

J. Mach. Learn. Res., 2017
Summary: 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, 2023
Xin Liu
exaly  

De-Biased Sparse PCA: Inference for Eigenstructure of Large Covariance Matrices

IEEE Transactions on Information Theory, 2021
Jana Janková, Sara van de Geer
exaly  

Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data

IEEE Transactions on Neural Networks and Learning Systems, 2019
Jin-Xing Liu   +2 more
exaly  

Joint Group Sparse PCA for Compressed Hyperspectral Imaging

IEEE Transactions on Image Processing, 2015
Zohaib Khan, Faisal Shafait, Ajmal Mian
exaly  

Convex Sparse PCA for Unsupervised Feature Learning

ACM Transactions on Knowledge Discovery From Data, 2017
Chengqi Zhang, Yi Yang, Feiping Nie
exaly  

Sparse Variable PCA Using Geodesic Steepest Descent

IEEE Transactions on Signal Processing, 2008
Magnús Örn Úlfarsson   +2 more
exaly  

Vector L₀ Sparse Variable PCA

IEEE Transactions on Signal Processing, 2011
Magnús Örn Úlfarsson, Victor Solo
exaly  

Tensor Sparse PCA and Face Recognition: A Novel Approach

SN Applied Sciences, 2020
Linh Tran, Trang Hoang, Lộc Trần
exaly  

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