A New Basis for Sparse Principal Component Analysis-Reference-Cited by-同舟云学术

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A New Basis for Sparse Principal Component Analysis

Published:2023-09-08 Issue: Volume: Page:1-14
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Chen Fan¹^ORCID,Rohe Karl¹

Affiliation:

1. Department of Statistics, University of Wisconsin–Madison, Madison, WI

Funder

National Science Foundation

Army Research Office

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2256502

Reference58 articles.

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

2. Augmented Implicitly Restarted Lanczos Bidiagonalization Methods

3. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

4. The “independent components” of natural scenes are edge filters

5. Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis

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