Reference health
Sequentially Learning Method for Sparse Principal Components with Variance Maximization
18/18 checkable references clean · checked 2026-09-05
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 18 checked references that resolve
resolves10.1016/j.ejor.2022.04.027
Operational research and artificial intelligence methods in banking
resolves10.1080/00207543.2021.1976433
Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework
resolves10.1007/s11081-020-09562-3
Alternating maximization: unifying framework for 8 sparse PCA formulations and efficient parallel codes
resolves10.1109/ACCESS.2022.3193783
Virtual Metrology in Semiconductor Fabrication Foundry Using Deep Learning Neural Networks
resolves10.1109/ACCESS.2021.3076193
A Realizable Overlay Virtual Metrology System in Semiconductor Manufacturing: Proposal, Challenges and Future Perspective
The 7 references without a DOI — listed, not checked
no DOI — not checkedOptimal solutions for sparse principal component analysis
no DOI — not checkedA direct formulation for sparse PCA using semidefinite programming
no DOI — not checkedExplainable ai for operational research: A defining framework, methods, applications, and a research agenda
no DOI — not checkedGeneralized power method for sparse principal component analysis
no DOI — not checkedref16
no DOI — not checkedDeflation methods for sparse PCA
no DOI — not checkedSpectral bounds for sparse PCA: Exact and greedy algorithms
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