Do Algorithms and Barriers for Sparse Principal Component Analysis Extend to Other Structured Settings?-Reference-Cited by-同舟云学术

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Do Algorithms and Barriers for Sparse Principal Component Analysis Extend to Other Structured Settings?

Published:2024 Issue: Volume:72 Page:3187-3200
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Wang Guanyi¹^ORCID,Lou Mengqi²^ORCID,Pananjady Ashwin³^ORCID

Affiliation:

1. Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore

2. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

3. H. Milton Stewart School of Industrial and Systems Engineering, Atlanta, GA, USA

Funder

National University of Singapore

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/78/10347386/10582906-aam.pdf

Reference63 articles.

1. Singular value decomposition for genome-wide expression data processing and modeling

2. High-dimensional analysis of semidefinite relaxations for sparse principal components

3. Stay on path: PCA along graph paths;Asteris,2015

4. Computational hardness of certifying bounds on constrained pca problems;Bandeira,2019

5. Model-Based Compressive Sensing

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Do Algorithms and Barriers for Sparse Principal Component Analysis Extend to Other Structured Settings?;IEEE Transactions on Signal Processing;2024

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